From 7ccbd19b8d67c3fa1ce8d6df58582eeb715b3b70 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Wed, 30 Sep 2026 00:23:45 -0400 Subject: [PATCH 1/5] flood pool: replay passes, trained pools do not grow; per_dam_l2 0 lifts the base recipe; journal Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015TepsZAS2LkFC5EmvuZrz9 --- .../reservoir/smoke_v2/flood_gate.json | 132 ++++++++++++++++++ .../reservoir/smoke_v2/s7_population.py | 53 +++++++ research/journal/2026-09.md | 22 +++ 3 files changed, 207 insertions(+) create mode 100644 experiments/reservoir/smoke_v2/flood_gate.json create mode 100644 experiments/reservoir/smoke_v2/s7_population.py diff --git a/experiments/reservoir/smoke_v2/flood_gate.json b/experiments/reservoir/smoke_v2/flood_gate.json new file mode 100644 index 00000000..77cf3c38 --- /dev/null +++ b/experiments/reservoir/smoke_v2/flood_gate.json @@ -0,0 +1,132 @@ +{ + "replay": { + "twin": "replay_FA_L2T0", + "engine": { + "m": 0.01458, + "lo": 0.00909, + "hi": 0.02653, + "up": 57, + "down": 12, + "n": 69 + }, + "offline": { + "m": 0.01394, + "lo": 0.00857, + "hi": 0.0211, + "up": 56, + "down": 13, + "n": 69 + }, + "created": { + "dams": 69, + "gt05": 0, + "ge5": 0, + "max": 0.0 + }, + "kept": 1.046, + "engine_vs_FA_T0_twin": { + "m": 0.0288, + "lo": 0.01582, + "hi": 0.03971, + "up": 61, + "down": 5, + "n": 69 + } + }, + "pass1": true, + "pair": [ + "S7pool", + "S7base" + ], + "pool_minus_base": { + "flood_control": { + "m": -0.00016, + "lo": -0.00046, + "hi": -1e-05, + "up": 69, + "down": 97, + "n": 166 + }, + "target_117": { + "m": -0.00025, + "lo": -0.00083, + "hi": -1e-05, + "up": 48, + "down": 69, + "n": 117 + }, + "dam_all": { + "m": -8e-05, + "lo": -0.00016, + "hi": 0.0, + "up": 210, + "down": 248, + "n": 458 + } + }, + "vs_off": { + "S7pool": { + "target_117": { + "m": 0.02765, + "lo": 0.01565, + "hi": 0.04488, + "up": 84, + "down": 33, + "n": 117 + }, + "dam_all": { + "m": 0.0125, + "lo": 0.00879, + "hi": 0.01873, + "up": 320, + "down": 138, + "n": 458 + } + }, + "S7base": { + "target_117": { + "m": 0.02376, + "lo": 0.00947, + "hi": 0.04531, + "up": 85, + "down": 32, + "n": 117 + }, + "dam_all": { + "m": 0.01153, + "lo": 0.00884, + "hi": 0.01737, + "up": 318, + "down": 140, + "n": 458 + } + } + }, + "controls_max": { + "S7pool": 0.0, + "S7base": 0.0 + }, + "created": { + "S7pool": { + "dams": 675, + "gt05": 72, + "ge5": 10, + "max": 0.13195 + }, + "S7base": { + "dams": 675, + "gt05": 76, + "ge5": 10, + "max": 0.23159 + } + }, + "pass2": false, + "pool_diag": { + "n": 172, + "median_max_F_days": 0.07, + "share_max_F_ge_1d": 0.047, + "captured_m3": 4018774093.365, + "evacuated_m3": 4017679627.101 + }, + "pools_moved": false +} \ No newline at end of file diff --git a/experiments/reservoir/smoke_v2/s7_population.py b/experiments/reservoir/smoke_v2/s7_population.py new file mode 100644 index 00000000..449e1488 --- /dev/null +++ b/experiments/reservoir/smoke_v2/s7_population.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python +"""S7 follow-up: (1) S6base vs S7base isolates per_dam_l2 (both rho 180); (2) S7base's per-DOR-bin median gains at +the 458 smoke dam gauges transferred to the 917 dammed gauges of the 2,365-gauge population (as population_ceiling.py).""" +import numpy as np, pandas as pd, zarr + +W = "/home/tbindas/projects/ddrs/.claude/worktrees/agent-a92e512a7c47c97b4/" +R = W + ".ddrs/runs/" +SM = "/home/tbindas/projects/ddrs/.claude/worktrees/reservoir-options/experiments/reservoir/smoke/" +RUNS = dict(off="2026-09-27T04-29-33Z", S4v3="2026-09-27T23-36-04Z", S6base="2026-09-30T01-53-00Z", + S7base="2026-09-30T02-46-12Z", S7pool="2026-09-30T02-46-23Z") + + +def load(run): + z = zarr.open(R + run + "-train-and-test/eval/predictions.zarr", mode="r") + ids = [bytes(r).decode().strip("\x00") for r in z["gage_ids"][:]] + return {s: i for i, s in enumerate(ids)}, z["predictions"][:].astype(float), z["observations"][:].astype(float) + + +def nse(p, o): + m = np.isfinite(p) & np.isfinite(o) + return 1 - ((p[m] - o[m]) ** 2).sum() / ((o[m] - o[m].mean()) ** 2).sum() + + +idx, _, O = load(RUNS["off"]) +N = {} +for k, r in RUNS.items(): + _, P, _ = load(r) + N[k] = pd.Series({s: nse(P[i], O[i]) for s, i in idx.items()}) +D = pd.DataFrame(N) +sm = pd.read_csv(SM + "smoke_gauges.csv", dtype={"STAID": str}).set_index("STAID") +onr = pd.read_csv(SM + "expected_release_fit.csv", dtype={"STAID": str}).set_index("STAID").on_reach.astype(str) == "True" +dam = [s for s in sm.index[sm.role == "dam"] if s in D.index] +tgt = [s for s in dam if onr.get(s, False) and sm.nid_dor[s] > 0.5] +for a in ["S4v3", "S6base", "S7base", "S7pool"]: + d = D[a] - D["off"] + print(f"{a:7s} vs off: target {d[tgt].median():+.4f} dam_all {d[dam].median():+.4f} median NSE {D[a].median():.4f}") +print(f"L2 effect at rho 180 (S7base - S6base): target {(D.S7base - D.S6base)[tgt].median():+.4f}, dam_all {(D.S7base - D.S6base)[dam].median():+.4f}") +bins = [(-1, 0.1), (0.1, 0.5), (0.5, 1), (1, 2), (2, 1e9)] +def binof(x): + for j, (lo, hi) in enumerate(bins): + if (lo < 0 or x > lo) and x <= hi: + return j + return len(bins) - 1 +g = (D.S7base - D.off)[dam] +kb = pd.Series({s: binof(sm.nid_dor[s]) for s in dam}) +gain = [float(g[kb == j].median()) for j in range(len(bins))] +print("S7base median gain by DOR bin:", [round(x, 4) for x in gain]) +P = pd.read_csv("/home/tbindas/projects/ddrs/.claude/worktrees/reservoir-options/experiments/reservoir/full_run/paired_full_run.csv", dtype={"STAID": str}).set_index("STAID") +base = P.nse_off +dammed = P.n_nid_ge10mcm > 0 +k = P.nid_dor.fillna(0).map(binof) +new = base + np.where(dammed, [gain[j] for j in k], 0.0) +print(f"population median {base.median():.4f} -> {new.median():.4f} (change {new.median() - base.median():+.4f}); target +0.0300") diff --git a/research/journal/2026-09.md b/research/journal/2026-09.md index 99bdb664..74f260ce 100644 --- a/research/journal/2026-09.md +++ b/research/journal/2026-09.md @@ -2056,3 +2056,25 @@ Do not run further leakance arms scored on skill. **What changed:** The user clarified that +0.03 is the median NSE over the full 2,365-gauge evaluation population. Every overnight entry above scored it on the 117 on-reach smoke gauges with DOR > 0.5; that target was my assumption. Their numbers stand as dam-effect measurements but not as progress toward the target. **What it means:** `experiments/reservoir/smoke_v2/population_ceiling.py`: the best smoke arm transferred by DOR bin moves the population median by +0.0017; a perfect dam model (every dammed gauge closes its gap to its matched control) by +0.0256. Dams alone cannot reach +0.03 at the population median. Next: flood-evacuation and irrigation-withdrawal laws against a runoff-rescaling null (`experiments/reservoir/laws_v6/`), with the population-median effect reported for each. + +--- + +### 2026-09-30 · flood pool: replay, S6 and S7 smoke pairs (`2026-09-30T01-52-25Z`, `01-52-45Z`, `02-16-02Z` replays; `01-53-00Z` S6base, `01-53-15Z` S6pool stopped; `02-46-12Z` S7base, `02-46-23Z` S7pool) + +**Facts** + +- binary `ddrs-v6-77aa4a0` (`dam-release-v2`), CPU, smoke set, workspace `agent-a92e512a7c47c97b4/.ddrs`, launched with `DDRS_JOURNAL_OFF=1` +- replays (zero-step, fixed tables, additive row, positivity cap): offline flood-pool fits for 69 on-reach flood-control dams with the pool (`01-52-25Z`), the same T0 without it (`01-52-45Z`), and the fair twin with the plain-bucket T0 (`02-16-02Z`, `fixed_FA_L2T0.csv`) +- training pairs (frozen routing, rho 180 / warmup 30, rule curve + per-dam T0, positivity cap): S6 with per_dam_l2 1e-3 (S6pool stopped at epoch 10 after review v6 HIGH 1: the L2 anchored at the pool-off init held z at 0.05 d); S7 with per_dam_l2 0 (the first S7 launch was killed by the 30-min background limit and relaunched detached) +- analysis: `experiments/reservoir/smoke_v2/flood_gate.{py,json}`, `s7_population.py` + +**Question:** Does the per-dam flood pool gain at flood-control dams in the engine, first with offline-fitted parameters (replay) and then when trained (pool vs no-pool pair)? The pass bar was fixed before results. + +**What we did:** + +- Replay: engine pool minus the fair no-pool twin vs offline FA minus L2 at the same 69 gauges. +- Training pair: S7pool minus S7base at the 166 smoke flood-control dam gauges; created water per dam; whether the pools grew. + +**Result:** Replay: +0.0146 [+0.0091, +0.0265] (57 up / 12 down) vs offline +0.0139, 0 water created: pass. Training pair: -0.0002 [-0.0005, -0.0000] (69 up / 97 down) at flood-control dams; pools did not grow (median largest fill 0.07 d; 4.7 % of pooled dams above 1 d); 10 dams create >= 5 % water in both S7 arms: fail. Against the no-dam arm, S7base gains +0.0238 at the 117 target gauges and +0.0115 at all 458 dam gauges (S6base +0.0155 / +0.0084; S4v3 +0.0168 / +0.0067); dropping per_dam_l2 alone adds +0.0050 / +0.0037 paired. S7base carried to the 2,365-gauge population by DOR bin: median 0.7391 -> 0.7425 (+0.0034). + +**Conclusion:** The flood-pool law works in the engine with fitted parameters, but training cannot grow a pool from the off init, so the trained pair is uninformative about the law and the pass bar is not met; full-population runs were not launched. The per-dam L2 at 1e-3 had been suppressing every per-dam parameter; without it the base recipe gains about 50 % more, at the cost of more created water (10 dams >= 5 %). Next options: initialise pools from the offline fits; population runs of the S7base recipe with a water-conserving floor; the population target still needs non-dam levers. From 16080ac7fc5b7315a0e30a3f33825834a8a0c081 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Wed, 30 Sep 2026 09:15:22 -0400 Subject: [PATCH 2/5] config: full-population S7 dam arms (carry floor, per_dam_l2 0) and rho-180 no-dam twins dam_release_full_s7_s{42,43}: dam_release_full_nopool_s{42,43} with release_head.per_dam_l2 0.0 (the S7base recipe) and dam_floor carry (mass-conserving; allowed without a flood pool). Joint training. dam_release_full_nodam_r180_s{42,43}: the same experiment block (rho 180, warmup 30, testing warmup 5) with use_reservoirs false and no dam table, ranges or release head, so each pair differs by the dam model alone. The earlier no-dam full runs used rho 90 and are not twins of these arms. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015TepsZAS2LkFC5EmvuZrz9 --- .../dam_release_full_nodam_r180_s42.yaml | 409 ++++++++++++++++ .../dam_release_full_nodam_r180_s43.yaml | 409 ++++++++++++++++ .../experiments/dam_release_full_s7_s42.yaml | 438 ++++++++++++++++++ .../experiments/dam_release_full_s7_s43.yaml | 438 ++++++++++++++++++ 4 files changed, 1694 insertions(+) create mode 100644 config/experiments/dam_release_full_nodam_r180_s42.yaml create mode 100644 config/experiments/dam_release_full_nodam_r180_s43.yaml create mode 100644 config/experiments/dam_release_full_s7_s42.yaml create mode 100644 config/experiments/dam_release_full_s7_s43.yaml diff --git a/config/experiments/dam_release_full_nodam_r180_s42.yaml b/config/experiments/dam_release_full_nodam_r180_s42.yaml new file mode 100644 index 00000000..5d2db374 --- /dev/null +++ b/config/experiments/dam_release_full_nodam_r180_s42.yaml @@ -0,0 +1,409 @@ +# dam_release_full_nodam_r180_s42 +# +# No-dam twin of dam_release_full_s7_s42.yaml: every reach routed as a channel +# (use_reservoirs false, no release head, no dam table) with the dam arm's experiment block +# exactly: rho 180, warmup 30, testing warmup 5, 50 epochs, lr schedule, nse-batch loss, grad +# accum 4, seed / np_seed 42, same gauges and windows. The pair differs by the dam model alone. +# The earlier no-dam full runs (dam_release_full_off.yaml: 2026-09-27T07-29-47Z s42, +# 2026-09-27T10-31-30Z s43) used rho 90 and warmup 5, so they are NOT twins of the rho-180 arms. +# Seed twin: dam_release_full_nodam_r180_s43.yaml (seed / np_seed only). +# +# Changes from dam_release_full_s7_s42.yaml (below, verbatim otherwise; its header is kept +# unchanged as provenance, so its dam-release text describes the matched arm, not this one): +# data_sources.reservoirs removed +# params.use_reservoirs: true -> false, params.reservoir_release removed +# params.parameter_ranges.reservoir_T0 / reservoir_a / reservoir_b removed +# release_head block removed +# +# Run from the worktree root, CPU, main workspace: +# /ddrs --config config/experiments/dam_release_full_nodam_r180_s42.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu +# +# --- source config (dam_release_full_s7_s42.yaml), header unchanged --- +# dam_release_full_s7_s42 +# +# Full-population JOINT training (routing head and dam release together, freeze_routing false) +# with the S7base release recipe and the mass-conserving dam floor, seed 42. The population +# test of the best dam recipe: the target (2026-09-30) is +0.03 over the no-dam median NSE of +# the 2,365-gauge evaluation population. +# Matched no-dam twin: dam_release_full_nodam_r180_s42.yaml (this experiment block exactly, +# use_reservoirs false). The pair differs by the dam model alone. +# Seed twin: dam_release_full_s7_s43.yaml (seed / np_seed only). +# +# Changes from dam_release_full_nopool_s42.yaml (below, verbatim otherwise): +# release_head.per_dam_l2: 1e-3 -> 0.0 (the S7base recipe, dam_release_smoke_S7base.yaml: +# the L2 anchored at init had held every per-dam parameter near it; without it the smoke +# base recipe gained +0.0238 at the 117 target gauges vs +0.0155 for S6base, journal +# 2026-09-30) +# release_head.dam_floor: forgive -> carry (mass-conserving floor: without it 10 smoke dams +# created >= 5 % of their water at the S28 clamp in S7base; carry repays the created +# volume out of the dam's later inflow. Allowed because there is no flood pool.) +# The recipe then: additive dam row with dam_row_positivity, per-dam T0, rule curve (max 0.5), +# per_dam_lr 0.02, feasibility penalty 5.0 (alpha 0.9), NID completion years, seasonal false, +# no flood pool; rho 180, warmup 30, testing warmup 5, 50 epochs, grad accum 4, nse-batch. +# +# Run from the worktree root (data_sources.reservoirs is relative), CPU, main workspace: +# /ddrs --config config/experiments/dam_release_full_s7_s42.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu +# +# --- source config (dam_release_full_nopool_s42.yaml), header unchanged --- +# dam_release_full_nopool_s42 +# +# No-pool twin of dam_release_full_pool_s42.yaml: full-population JOINT training (routing head +# and dam release together, freeze_routing false) with the S6 release settings and NO flood pool, +# seed 42. The two arms differ by release_head.flood_pool alone. +# +# dam_release_full_learned.yaml (below, verbatim otherwise; 2,365 gauges of gages_3000, train +# 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30) with the release settings of the smoke +# arm S6base (config/experiments/dam_release_smoke_S6base.yaml): +# release_head: seasonal false, dam_row additive, dam_row_positivity true, dam_floor forgive, +# rule_curve true (max 0.5), per_dam_t0 true, per_dam_lr 0.02, per_dam_l2 1e-3, +# rule_curve_penalty 5.0 (alpha 0.9) +# params.parameter_ranges.reservoir_T0 [0.0001, 365] (the additive row's box, as S4v3/S6) +# experiment.rho 180, experiment.warmup 30 (the pool arm's windows, kept so the twins differ by +# the pool alone), testing.warmup 5 +# no routing_checkpoint, freeze_routing false: both heads start at the seed's init and train +# together (dam_release_full_learned's protocol) +# seed / np_seed 42 +# PREPARED, NOT LAUNCHED (2026-09-29). +# +# Run from the worktree root, CPU: +# ddrs --config config/experiments/dam_release_full_nopool_s42.yaml --workspace \ +# run --workflow train-and-test --backend cpu +# +# --- source config (dam_release_full_learned.yaml), header unchanged --- +# dam_release_full_learned +# +# Full-population learned-release arm: the sr_n0_gamma recipe plus the learned dam +# release (params.reservoir_release: learned) at every NID dam >= 10 MCM whose COMID is +# in a batch network (experiments/reservoir/release_head/dam_features.csv, 1,024 COMIDs). +# Each dam is a seasonal linear reservoir, storage-conserving on S = T(t) Q, with +# T(t) = max(T0 exp(a sin w + b cos w), 1 h), w = 2 pi doy / 365.25, +# and (T0, a, b) per dam from a release head on the dam's NID features, trained +# jointly with the KAN routing head from gauge observations only. No observed dam +# data (releases, schedules, caps) enters. Matched arm: dam_release_full_off.yaml. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header except the dam table, the release block and the release ranges): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_learned.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 180 # S6: 90 -> 180 (the flood pool's ~60-day timescale) + shuffle: true + warmup: 30 # S6: 5 -> 30 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + # No dams: every reach is a channel (the matched dam arm sets use_reservoirs true, + # reservoir_release learned). + use_reservoirs: false + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1995/10/01 + end_time: 2010/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode + warmup: 5 # the test phase skips 5 days, as every earlier arm's diff --git a/config/experiments/dam_release_full_nodam_r180_s43.yaml b/config/experiments/dam_release_full_nodam_r180_s43.yaml new file mode 100644 index 00000000..978dcad1 --- /dev/null +++ b/config/experiments/dam_release_full_nodam_r180_s43.yaml @@ -0,0 +1,409 @@ +# dam_release_full_nodam_r180_s43 +# +# No-dam twin of dam_release_full_s7_s43.yaml: every reach routed as a channel +# (use_reservoirs false, no release head, no dam table) with the dam arm's experiment block +# exactly: rho 180, warmup 30, testing warmup 5, 50 epochs, lr schedule, nse-batch loss, grad +# accum 4, seed / np_seed 43, same gauges and windows. The pair differs by the dam model alone. +# The earlier no-dam full runs (dam_release_full_off.yaml: 2026-09-27T07-29-47Z s42, +# 2026-09-27T10-31-30Z s43) used rho 90 and warmup 5, so they are NOT twins of the rho-180 arms. +# Seed twin: dam_release_full_nodam_r180_s42.yaml (seed / np_seed only). +# +# Changes from dam_release_full_s7_s43.yaml (below, verbatim otherwise; its header is kept +# unchanged as provenance, so its dam-release text describes the matched arm, not this one): +# data_sources.reservoirs removed +# params.use_reservoirs: true -> false, params.reservoir_release removed +# params.parameter_ranges.reservoir_T0 / reservoir_a / reservoir_b removed +# release_head block removed +# +# Run from the worktree root, CPU, main workspace: +# /ddrs --config config/experiments/dam_release_full_nodam_r180_s43.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu +# +# --- source config (dam_release_full_s7_s43.yaml), header unchanged --- +# dam_release_full_s7_s43 +# +# Full-population JOINT training (routing head and dam release together, freeze_routing false) +# with the S7base release recipe and the mass-conserving dam floor, seed 43. The population +# test of the best dam recipe: the target (2026-09-30) is +0.03 over the no-dam median NSE of +# the 2,365-gauge evaluation population. +# Matched no-dam twin: dam_release_full_nodam_r180_s43.yaml (this experiment block exactly, +# use_reservoirs false). The pair differs by the dam model alone. +# Seed twin: dam_release_full_s7_s42.yaml (seed / np_seed only). +# +# Changes from dam_release_full_nopool_s43.yaml (below, verbatim otherwise): +# release_head.per_dam_l2: 1e-3 -> 0.0 (the S7base recipe, dam_release_smoke_S7base.yaml: +# the L2 anchored at init had held every per-dam parameter near it; without it the smoke +# base recipe gained +0.0238 at the 117 target gauges vs +0.0155 for S6base, journal +# 2026-09-30) +# release_head.dam_floor: forgive -> carry (mass-conserving floor: without it 10 smoke dams +# created >= 5 % of their water at the S28 clamp in S7base; carry repays the created +# volume out of the dam's later inflow. Allowed because there is no flood pool.) +# The recipe then: additive dam row with dam_row_positivity, per-dam T0, rule curve (max 0.5), +# per_dam_lr 0.02, feasibility penalty 5.0 (alpha 0.9), NID completion years, seasonal false, +# no flood pool; rho 180, warmup 30, testing warmup 5, 50 epochs, grad accum 4, nse-batch. +# +# Run from the worktree root (data_sources.reservoirs is relative), CPU, main workspace: +# /ddrs --config config/experiments/dam_release_full_s7_s43.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu +# +# --- source config (dam_release_full_nopool_s43.yaml), header unchanged --- +# dam_release_full_nopool_s43 +# +# No-pool twin of dam_release_full_pool_s43.yaml: full-population JOINT training (routing head +# and dam release together, freeze_routing false) with the S6 release settings and NO flood pool, +# seed 43. The two arms differ by release_head.flood_pool alone. +# +# dam_release_full_learned.yaml (below, verbatim otherwise; 2,365 gauges of gages_3000, train +# 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30) with the release settings of the smoke +# arm S6base (config/experiments/dam_release_smoke_S6base.yaml): +# release_head: seasonal false, dam_row additive, dam_row_positivity true, dam_floor forgive, +# rule_curve true (max 0.5), per_dam_t0 true, per_dam_lr 0.02, per_dam_l2 1e-3, +# rule_curve_penalty 5.0 (alpha 0.9) +# params.parameter_ranges.reservoir_T0 [0.0001, 365] (the additive row's box, as S4v3/S6) +# experiment.rho 180, experiment.warmup 30 (the pool arm's windows, kept so the twins differ by +# the pool alone), testing.warmup 5 +# no routing_checkpoint, freeze_routing false: both heads start at the seed's init and train +# together (dam_release_full_learned's protocol) +# seed / np_seed 43 +# PREPARED, NOT LAUNCHED (2026-09-29). +# +# Run from the worktree root, CPU: +# ddrs --config config/experiments/dam_release_full_nopool_s43.yaml --workspace \ +# run --workflow train-and-test --backend cpu +# +# --- source config (dam_release_full_learned.yaml), header unchanged --- +# dam_release_full_learned +# +# Full-population learned-release arm: the sr_n0_gamma recipe plus the learned dam +# release (params.reservoir_release: learned) at every NID dam >= 10 MCM whose COMID is +# in a batch network (experiments/reservoir/release_head/dam_features.csv, 1,024 COMIDs). +# Each dam is a seasonal linear reservoir, storage-conserving on S = T(t) Q, with +# T(t) = max(T0 exp(a sin w + b cos w), 1 h), w = 2 pi doy / 365.25, +# and (T0, a, b) per dam from a release head on the dam's NID features, trained +# jointly with the KAN routing head from gauge observations only. No observed dam +# data (releases, schedules, caps) enters. Matched arm: dam_release_full_off.yaml. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header except the dam table, the release block and the release ranges): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_learned.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 43 +np_seed: 43 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 180 # S6: 90 -> 180 (the flood pool's ~60-day timescale) + shuffle: true + warmup: 30 # S6: 5 -> 30 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + # No dams: every reach is a channel (the matched dam arm sets use_reservoirs true, + # reservoir_release learned). + use_reservoirs: false + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1995/10/01 + end_time: 2010/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode + warmup: 5 # the test phase skips 5 days, as every earlier arm's diff --git a/config/experiments/dam_release_full_s7_s42.yaml b/config/experiments/dam_release_full_s7_s42.yaml new file mode 100644 index 00000000..b5385987 --- /dev/null +++ b/config/experiments/dam_release_full_s7_s42.yaml @@ -0,0 +1,438 @@ +# dam_release_full_s7_s42 +# +# Full-population JOINT training (routing head and dam release together, freeze_routing false) +# with the S7base release recipe and the mass-conserving dam floor, seed 42. The population +# test of the best dam recipe: the target (2026-09-30) is +0.03 over the no-dam median NSE of +# the 2,365-gauge evaluation population. +# Matched no-dam twin: dam_release_full_nodam_r180_s42.yaml (this experiment block exactly, +# use_reservoirs false). The pair differs by the dam model alone. +# Seed twin: dam_release_full_s7_s43.yaml (seed / np_seed only). +# +# Changes from dam_release_full_nopool_s42.yaml (below, verbatim otherwise): +# release_head.per_dam_l2: 1e-3 -> 0.0 (the S7base recipe, dam_release_smoke_S7base.yaml: +# the L2 anchored at init had held every per-dam parameter near it; without it the smoke +# base recipe gained +0.0238 at the 117 target gauges vs +0.0155 for S6base, journal +# 2026-09-30) +# release_head.dam_floor: forgive -> carry (mass-conserving floor: without it 10 smoke dams +# created >= 5 % of their water at the S28 clamp in S7base; carry repays the created +# volume out of the dam's later inflow. Allowed because there is no flood pool.) +# The recipe then: additive dam row with dam_row_positivity, per-dam T0, rule curve (max 0.5), +# per_dam_lr 0.02, feasibility penalty 5.0 (alpha 0.9), NID completion years, seasonal false, +# no flood pool; rho 180, warmup 30, testing warmup 5, 50 epochs, grad accum 4, nse-batch. +# +# Run from the worktree root (data_sources.reservoirs is relative), CPU, main workspace: +# /ddrs --config config/experiments/dam_release_full_s7_s42.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu +# +# --- source config (dam_release_full_nopool_s42.yaml), header unchanged --- +# dam_release_full_nopool_s42 +# +# No-pool twin of dam_release_full_pool_s42.yaml: full-population JOINT training (routing head +# and dam release together, freeze_routing false) with the S6 release settings and NO flood pool, +# seed 42. The two arms differ by release_head.flood_pool alone. +# +# dam_release_full_learned.yaml (below, verbatim otherwise; 2,365 gauges of gages_3000, train +# 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30) with the release settings of the smoke +# arm S6base (config/experiments/dam_release_smoke_S6base.yaml): +# release_head: seasonal false, dam_row additive, dam_row_positivity true, dam_floor forgive, +# rule_curve true (max 0.5), per_dam_t0 true, per_dam_lr 0.02, per_dam_l2 1e-3, +# rule_curve_penalty 5.0 (alpha 0.9) +# params.parameter_ranges.reservoir_T0 [0.0001, 365] (the additive row's box, as S4v3/S6) +# experiment.rho 180, experiment.warmup 30 (the pool arm's windows, kept so the twins differ by +# the pool alone), testing.warmup 5 +# no routing_checkpoint, freeze_routing false: both heads start at the seed's init and train +# together (dam_release_full_learned's protocol) +# seed / np_seed 42 +# PREPARED, NOT LAUNCHED (2026-09-29). +# +# Run from the worktree root, CPU: +# ddrs --config config/experiments/dam_release_full_nopool_s42.yaml --workspace \ +# run --workflow train-and-test --backend cpu +# +# --- source config (dam_release_full_learned.yaml), header unchanged --- +# dam_release_full_learned +# +# Full-population learned-release arm: the sr_n0_gamma recipe plus the learned dam +# release (params.reservoir_release: learned) at every NID dam >= 10 MCM whose COMID is +# in a batch network (experiments/reservoir/release_head/dam_features.csv, 1,024 COMIDs). +# Each dam is a seasonal linear reservoir, storage-conserving on S = T(t) Q, with +# T(t) = max(T0 exp(a sin w + b cos w), 1 h), w = 2 pi doy / 365.25, +# and (T0, a, b) per dam from a release head on the dam's NID features, trained +# jointly with the KAN routing head from gauge observations only. No observed dam +# data (releases, schedules, caps) enters. Matched arm: dam_release_full_off.yaml. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header except the dam table, the release block and the release ranges): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_learned.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + # Learned dam release: the dam feature table (1,024 COMIDs; NID >= 10 MCM, + # snap class A-D; experiments/reservoir/release_head/build_dam_features.py). + reservoirs: experiments/reservoir/release_head/dam_features.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 180 # S6: 90 -> 180 (the flood pool's ~60-day timescale) + shuffle: true + warmup: 30 # S6: 5 -> 30 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + # Learned dam release (off in the matched arm). See the header. + use_reservoirs: true + reservoir_release: learned + parameter_ranges: + # Release-head boxes: T0 in days (log space), a and b linear. + reservoir_T0: [0.0001, 365.0] # additive row: any lo > 0 (S4v3/S6) + reservoir_a: [-2.0, 2.0] + reservoir_b: [-2.0, 2.0] + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# The dam release head: a second KanHead (Linear(F,H) -> KanLayer x N -> Linear(H,3) +# -> Sigmoid) emitting T0, a, b per dam from these feature-table columns. Init: +# T0 = 4.5 h, a = b = 0 for every dam (src/nn/release_head.rs). +release_head: + hidden_size: 8 + num_hidden_layers: 1 + grid: 5 + k: 3 + # Release settings of the smoke arm S7base (joint training: no routing_checkpoint, not frozen) + # with the carried (mass-conserving) dam floor. + seasonal: false + dam_row: additive + dam_row_positivity: true + dam_floor: carry # S7 full: forgive -> carry (created water repaid from later inflow) + freeze_routing: false + rule_curve: true + rule_curve_max: 0.5 + per_dam_lr: 0.02 + per_dam_l2: 0.0 # S7 (2026-09-30): no per-dam L2 (it anchored the per-dam parameters at init) + rule_curve_penalty: 5.0 + rule_curve_alpha: 0.9 + per_dam_t0: true + input_var_names: + - log10_storage + - log10_storage_max + - log10_surface + - log10_drainage + - log10_storage_per_area + - log10_max_discharge + - height + - year + - log10_storage_max_missing + - log10_surface_missing + - log10_max_discharge_missing + - year_missing + - purpose_flood + - purpose_hydro + - purpose_supply + - purpose_irrigation + - purpose_recreation + - purpose_navigation + - purpose_other + + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1995/10/01 + end_time: 2010/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode + warmup: 5 # the test phase skips 5 days, as every earlier arm's diff --git a/config/experiments/dam_release_full_s7_s43.yaml b/config/experiments/dam_release_full_s7_s43.yaml new file mode 100644 index 00000000..a5ba2446 --- /dev/null +++ b/config/experiments/dam_release_full_s7_s43.yaml @@ -0,0 +1,438 @@ +# dam_release_full_s7_s43 +# +# Full-population JOINT training (routing head and dam release together, freeze_routing false) +# with the S7base release recipe and the mass-conserving dam floor, seed 43. The population +# test of the best dam recipe: the target (2026-09-30) is +0.03 over the no-dam median NSE of +# the 2,365-gauge evaluation population. +# Matched no-dam twin: dam_release_full_nodam_r180_s43.yaml (this experiment block exactly, +# use_reservoirs false). The pair differs by the dam model alone. +# Seed twin: dam_release_full_s7_s42.yaml (seed / np_seed only). +# +# Changes from dam_release_full_nopool_s43.yaml (below, verbatim otherwise): +# release_head.per_dam_l2: 1e-3 -> 0.0 (the S7base recipe, dam_release_smoke_S7base.yaml: +# the L2 anchored at init had held every per-dam parameter near it; without it the smoke +# base recipe gained +0.0238 at the 117 target gauges vs +0.0155 for S6base, journal +# 2026-09-30) +# release_head.dam_floor: forgive -> carry (mass-conserving floor: without it 10 smoke dams +# created >= 5 % of their water at the S28 clamp in S7base; carry repays the created +# volume out of the dam's later inflow. Allowed because there is no flood pool.) +# The recipe then: additive dam row with dam_row_positivity, per-dam T0, rule curve (max 0.5), +# per_dam_lr 0.02, feasibility penalty 5.0 (alpha 0.9), NID completion years, seasonal false, +# no flood pool; rho 180, warmup 30, testing warmup 5, 50 epochs, grad accum 4, nse-batch. +# +# Run from the worktree root (data_sources.reservoirs is relative), CPU, main workspace: +# /ddrs --config config/experiments/dam_release_full_s7_s43.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu +# +# --- source config (dam_release_full_nopool_s43.yaml), header unchanged --- +# dam_release_full_nopool_s43 +# +# No-pool twin of dam_release_full_pool_s43.yaml: full-population JOINT training (routing head +# and dam release together, freeze_routing false) with the S6 release settings and NO flood pool, +# seed 43. The two arms differ by release_head.flood_pool alone. +# +# dam_release_full_learned.yaml (below, verbatim otherwise; 2,365 gauges of gages_3000, train +# 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30) with the release settings of the smoke +# arm S6base (config/experiments/dam_release_smoke_S6base.yaml): +# release_head: seasonal false, dam_row additive, dam_row_positivity true, dam_floor forgive, +# rule_curve true (max 0.5), per_dam_t0 true, per_dam_lr 0.02, per_dam_l2 1e-3, +# rule_curve_penalty 5.0 (alpha 0.9) +# params.parameter_ranges.reservoir_T0 [0.0001, 365] (the additive row's box, as S4v3/S6) +# experiment.rho 180, experiment.warmup 30 (the pool arm's windows, kept so the twins differ by +# the pool alone), testing.warmup 5 +# no routing_checkpoint, freeze_routing false: both heads start at the seed's init and train +# together (dam_release_full_learned's protocol) +# seed / np_seed 43 +# PREPARED, NOT LAUNCHED (2026-09-29). +# +# Run from the worktree root, CPU: +# ddrs --config config/experiments/dam_release_full_nopool_s43.yaml --workspace \ +# run --workflow train-and-test --backend cpu +# +# --- source config (dam_release_full_learned.yaml), header unchanged --- +# dam_release_full_learned +# +# Full-population learned-release arm: the sr_n0_gamma recipe plus the learned dam +# release (params.reservoir_release: learned) at every NID dam >= 10 MCM whose COMID is +# in a batch network (experiments/reservoir/release_head/dam_features.csv, 1,024 COMIDs). +# Each dam is a seasonal linear reservoir, storage-conserving on S = T(t) Q, with +# T(t) = max(T0 exp(a sin w + b cos w), 1 h), w = 2 pi doy / 365.25, +# and (T0, a, b) per dam from a release head on the dam's NID features, trained +# jointly with the KAN routing head from gauge observations only. No observed dam +# data (releases, schedules, caps) enters. Matched arm: dam_release_full_off.yaml. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header except the dam table, the release block and the release ranges): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_learned.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 43 +np_seed: 43 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + # Learned dam release: the dam feature table (1,024 COMIDs; NID >= 10 MCM, + # snap class A-D; experiments/reservoir/release_head/build_dam_features.py). + reservoirs: experiments/reservoir/release_head/dam_features.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 180 # S6: 90 -> 180 (the flood pool's ~60-day timescale) + shuffle: true + warmup: 30 # S6: 5 -> 30 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + # Learned dam release (off in the matched arm). See the header. + use_reservoirs: true + reservoir_release: learned + parameter_ranges: + # Release-head boxes: T0 in days (log space), a and b linear. + reservoir_T0: [0.0001, 365.0] # additive row: any lo > 0 (S4v3/S6) + reservoir_a: [-2.0, 2.0] + reservoir_b: [-2.0, 2.0] + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# The dam release head: a second KanHead (Linear(F,H) -> KanLayer x N -> Linear(H,3) +# -> Sigmoid) emitting T0, a, b per dam from these feature-table columns. Init: +# T0 = 4.5 h, a = b = 0 for every dam (src/nn/release_head.rs). +release_head: + hidden_size: 8 + num_hidden_layers: 1 + grid: 5 + k: 3 + # Release settings of the smoke arm S7base (joint training: no routing_checkpoint, not frozen) + # with the carried (mass-conserving) dam floor. + seasonal: false + dam_row: additive + dam_row_positivity: true + dam_floor: carry # S7 full: forgive -> carry (created water repaid from later inflow) + freeze_routing: false + rule_curve: true + rule_curve_max: 0.5 + per_dam_lr: 0.02 + per_dam_l2: 0.0 # S7 (2026-09-30): no per-dam L2 (it anchored the per-dam parameters at init) + rule_curve_penalty: 5.0 + rule_curve_alpha: 0.9 + per_dam_t0: true + input_var_names: + - log10_storage + - log10_storage_max + - log10_surface + - log10_drainage + - log10_storage_per_area + - log10_max_discharge + - height + - year + - log10_storage_max_missing + - log10_surface_missing + - log10_max_discharge_missing + - year_missing + - purpose_flood + - purpose_hydro + - purpose_supply + - purpose_irrigation + - purpose_recreation + - purpose_navigation + - purpose_other + + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1995/10/01 + end_time: 2010/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode + warmup: 5 # the test phase skips 5 days, as every earlier arm's From b66ea8541e9c63dec1c81610c988e360adb663b0 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Wed, 30 Sep 2026 11:50:31 -0400 Subject: [PATCH 3/5] levers: non-dam levers for the 2,365-gauge population median (offline, Phase A) Honest runoff multipliers (fitted on training years) move the population median by +0.0005 to +0.0008: the model is volume-unbiased on 1982-1995 (pred/obs 1.004) and the test-period excess is a drought/drift in Q'. gamma = 0 is worth about +0.002 (+0.006 with learned p, q; one seed); a two-seed ensemble +0.001. A training-year-weighted blend of three Q' products (UH 0.50, DIST 0.25, LSTM 0.25) gives +0.0259 [+0.0221, +0.0309] at the population median (KGE about flat; paired KGE -0.004); with gamma = 0 variants +0.0297. Pre-registration in prereg.txt; report in report.txt. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015TepsZAS2LkFC5EmvuZrz9 --- experiments/levers/combine.json | 437 +++ experiments/levers/combine.py | 91 + experiments/levers/combine.txt | 42 + experiments/levers/combine_ens.py | 26 + experiments/levers/combine_ens.txt | 14 + experiments/levers/common.py | 104 + experiments/levers/convergence_ckpt.py | 38 + experiments/levers/convergence_ckpt.txt | 9 + experiments/levers/diff_configs.py | 27 + experiments/levers/ens_detail.py | 42 + experiments/levers/ens_detail.txt | 15 + experiments/levers/ensemble_weights.json | 151 ++ experiments/levers/ensemble_weights.py | 112 + experiments/levers/ensemble_weights.txt | 17 + .../levers/ensemble_weights_3member.json | 151 ++ .../levers/ensemble_weights_3member.txt | 17 + .../levers/ensemble_weights_uh_lstm.txt | 16 + experiments/levers/gauge_table.py | 80 + experiments/levers/gauges_table.csv | 2366 +++++++++++++++++ experiments/levers/inventory_runs.py | 41 + experiments/levers/launch_replay.sh | 11 + .../loss_curve_2026-09-12T03-53-34Z.csv | 51 + .../loss_curve_2026-09-12T23-39-06Z.csv | 51 + .../loss_curve_2026-09-27T07-29-47Z.csv | 51 + .../loss_curve_2026-09-27T10-31-30Z.csv | 51 + experiments/levers/loss_robust.py | 25 + experiments/levers/loss_robust.txt | 16 + experiments/levers/make_ckpt_configs.py | 35 + experiments/levers/make_plan_configs.py | 17 + experiments/levers/make_replay_configs.py | 30 + experiments/levers/make_store_configs.py | 27 + experiments/levers/mult_attr_gbm_s42.npy | Bin 0 -> 19048 bytes experiments/levers/mult_attr_gbm_s43.npy | Bin 0 -> 19048 bytes experiments/levers/peek.py | 9 + experiments/levers/peek_attrs.py | 11 + experiments/levers/plan_dist.log | 24 + experiments/levers/plan_hydrodl.log | 23 + experiments/levers/plan_hydrodl_train10.yaml | 323 +++ experiments/levers/plan_lstm.log | 23 + experiments/levers/plan_lumped.log | 24 + experiments/levers/plan_lumped_train10.yaml | 323 +++ experiments/levers/plan_uh.log | 24 + experiments/levers/plan_uh_train10.yaml | 329 +++ experiments/levers/prereg.txt | 16 + experiments/levers/queue.log | 11 + experiments/levers/queue.out | 0 experiments/levers/queue.sh | 19 + experiments/levers/queue2.out | 0 experiments/levers/replay_check.py | 43 + .../replay_check_2026-09-30T13-13-59Z.txt | 16 + .../replay_check_2026-09-30T13-50-59Z.txt | 16 + experiments/levers/replay_dist_trainyears.log | 264 ++ .../levers/replay_dist_trainyears.yaml | 325 +++ experiments/levers/replay_g0_trainyears.yaml | 294 ++ experiments/levers/replay_lstm_trainyears.log | 264 ++ .../levers/replay_lstm_trainyears.yaml | 325 +++ experiments/levers/replay_s42.log | 388 +++ experiments/levers/replay_s42_e10_test5y.yaml | 331 +++ experiments/levers/replay_s42_e25_test5y.log | 144 + experiments/levers/replay_s42_e25_test5y.yaml | 331 +++ experiments/levers/replay_s42_e40_test5y.log | 141 + experiments/levers/replay_s42_e40_test5y.yaml | 331 +++ experiments/levers/replay_s43_trainyears.log | 385 +++ experiments/levers/replay_s43_trainyears.yaml | 332 +++ experiments/levers/replay_trainyears_s42.yaml | 332 +++ experiments/levers/replay_trainyears_s43.yaml | 332 +++ experiments/levers/report.txt | 161 ++ experiments/levers/rerun.err | 8 + experiments/levers/rerun.log | 31 + experiments/levers/rerun_all.sh | 23 + experiments/levers/store_ensembles.py | 37 + experiments/levers/store_ensembles.txt | 18 + experiments/levers/store_selection.py | 71 + experiments/levers/store_selection.txt | 18 + experiments/levers/timing.py | 115 + experiments/levers/timing_s42.json | 142 + experiments/levers/timing_s42.txt | 14 + experiments/levers/timing_s43.json | 142 + experiments/levers/timing_s43.txt | 14 + experiments/levers/variance.json | 499 ++++ experiments/levers/variance.py | 242 ++ experiments/levers/variance.txt | 118 + experiments/levers/variance_by_gauge.csv | 2366 +++++++++++++++++ experiments/levers/variance_gamma.py | 62 + experiments/levers/variance_gamma.txt | 21 + experiments/levers/volume.py | 194 ++ experiments/levers/volume_by_gauge_s42.csv | 2366 +++++++++++++++++ experiments/levers/volume_by_gauge_s43.csv | 2366 +++++++++++++++++ experiments/levers/volume_by_year.py | 29 + .../volume_by_year_2026-09-27T07-29-47Z.txt | 30 + .../volume_by_year_2026-09-27T10-31-30Z.txt | 30 + experiments/levers/volume_s42.json | 713 +++++ experiments/levers/volume_s42.txt | 63 + experiments/levers/volume_s43.json | 713 +++++ experiments/levers/volume_s43.txt | 63 + experiments/levers/volume_state.py | 125 + experiments/levers/volume_state_s42.json | 170 ++ experiments/levers/volume_state_s42.txt | 16 + experiments/levers/volume_state_s43.json | 170 ++ experiments/levers/volume_state_s43.txt | 16 + 100 files changed, 21080 insertions(+) create mode 100644 experiments/levers/combine.json create mode 100644 experiments/levers/combine.py create mode 100644 experiments/levers/combine.txt create mode 100644 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"mult_median": 1.0 + }, + "n only (gamma = 0, channel fixed), seed 42 | none": { + "nse": { + "name": "n only (gamma = 0, channel fixed), seed 42 | none", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7409165798527144, + "pop_change": 0.0018046501972226459, + "ci_lo": -0.0005111463180353604, + "ci_hi": 0.004854496037369285, + "paired_median": 0.0003894465421172555, + "share_up": 0.5797040169133192, + "share_down": 0.42029598308668076, + "n": 2365 + }, + "kge": { + "name": "n only (gamma = 0, channel fixed), seed 42 | none", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7612137576132139, + "pop_change": 0.0021402750436021467, + "ci_lo": 4.820222975574499e-05, + "ci_hi": 0.004259286869362144, + "paired_median": 0.00017030410624829795, + "share_up": 0.5716701902748414, + "share_down": 0.42832980972515855, + "n": 2365 + }, + "mult_median": 1.0 + } +} \ No newline at end of file diff --git a/experiments/levers/combine.py b/experiments/levers/combine.py new file mode 100644 index 00000000..35ff2fab --- /dev/null +++ b/experiments/levers/combine.py @@ -0,0 +1,91 @@ +"""Combine the non-dam levers on per-gauge test predictions (WY1996-2010), against seed 42's 0.7391 / 0.7591. + +Arms are equal-weight ensembles of runs; a runoff multiplier is fitted on the arm's own TRAINING-year predictions +(zero-step replays, WY1982-1995) and applied to its test predictions. Pre-registered primary multipliers (prereg.txt): +global = median per-gauge LS k*; regional = per-HUC2 median k*; ceiling = per-gauge k*. +Usage: combine.py key=testrun:trainreplay ... e.g. s42=2026-09-27T07-29-47Z-train-and-test:2026-09-30T13-13-59Z-train-and-test +(trainreplay may be '-'). Writes combine.txt / combine.json. +""" +import json +import sys + +import numpy as np +import pandas as pd + +import common as C + +runs = dict(a.split("=", 1) for a in sys.argv[1:]) +runs = {k: tuple(v.split(":")) for k, v in runs.items()} +out, res = [], {} +say = out.append +ids, tt, P42, O = C.load_preds(C.S42) +wy = C.water_year(tt) +tst = (wy >= 1996) & (wy <= 2010) +Ote = O[:, tst] +B = C.metrics(P42[:, tst], Ote) +bn, bk = B.nse.values, B.kge.values +G = pd.read_csv(C.HERE / "gauges_table.csv", dtype={"STAID": str, "HUC02": str}).set_index("STAID").reindex(ids) +huc = G.HUC02.values +cache = {} + + +def test_pred(run): + if run not in cache: + i, t, P, _ = C.load_preds(run) + assert (t == tt).all() + cache[run] = C.align(i, P, ids)[:, tst] + return cache[run] + + +def train_pred(run): + k = ("tr", run) + if k not in cache: + i, t, P, Ob = C.load_preds(run) + w = C.water_year(t) + m = (w >= 1982) & (w <= 1995) & (t >= pd.Timestamp("1981-11-01")) + cache[k] = (C.align(i, P, ids)[:, m], C.align(i, Ob, ids)[:, m]) + return cache[k] + + +def multipliers(Ptr, Otr): + k = C.ls_scale(Ptr, Otr) + ok = np.isfinite(k) & (k > 0.2) & (k < 5) & np.isfinite(C.metrics(Ptr, Otr).nse.values) + glob = np.full(len(ids), np.nanmedian(k[ok])) + reg = np.ones(len(ids)) + for h in set(huc): + r = huc == h + reg[r] = np.nanmedian(k[r & ok]) if (r & ok).any() else glob[0] + gauge = np.where(ok, k, 1.0) + return dict(none=np.ones(len(ids)), global_k=glob, huc2_k=reg, gauge_k=gauge) + + +ARMS = { + "working recipe, seed 42": ["s42"], + "working recipe, seed 43": ["s43"], + "working recipe, two-seed ensemble": ["s42", "s43"], + "gamma = 0, p and q learned (g0), seed 42": ["g0"], + "g0 + working recipe s42 + s43 (3-member ensemble)": ["g0", "s42", "s43"], + "n only (gamma = 0, channel fixed), seed 42": ["nonly"], +} +for name, keys in ARMS.items(): + if not all(k in runs for k in keys): + continue + Pte = np.mean([test_pred(runs[k][0]) for k in keys], axis=0) + have_tr = all(runs[k][1] != "-" for k in keys) + if have_tr: + trs = [train_pred(runs[k][1]) for k in keys] + Ptr = np.mean([a for a, _ in trs], axis=0) + mults = multipliers(Ptr, trs[0][1]) + else: + mults = dict(none=np.ones(len(ids))) + say(f"\n== {name} ({'+'.join(keys)})") + for mk, m in mults.items(): + M = C.metrics(Pte * m[:, None], Ote) + s = C.summarize(f"{name} | {mk}", M.nse.values, bn) + sk = C.summarize(f"{name} | {mk}", M.kge.values, bk, "KGE") + say(C.fmt(s)) + say(C.fmt(sk)) + res[f"{name} | {mk}"] = dict(nse=s, kge=sk, mult_median=float(np.median(m))) +open(C.HERE / "combine.txt", "w").write("\n".join(out) + "\n") +json.dump(res, open(C.HERE / "combine.json", "w"), indent=1, default=float) +print("\n".join(out)) diff --git a/experiments/levers/combine.txt b/experiments/levers/combine.txt new file mode 100644 index 00000000..b6c3e9cb --- /dev/null +++ b/experiments/levers/combine.txt @@ -0,0 +1,42 @@ + +== working recipe, seed 42 (s42) + working recipe, seed 42 | none NSE median 0.7391 -> 0.7391 pop +0.0000 [-0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + working recipe, seed 42 | none KGE median 0.7591 -> 0.7591 pop -0.0000 [-0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + working recipe, seed 42 | global_k NSE median 0.7391 -> 0.7396 pop +0.0005 [-0.0006,+0.0016] paired +0.0004 up 62.9% down 37.1% + working recipe, seed 42 | global_k KGE median 0.7591 -> 0.7595 pop +0.0004 [-0.0016,+0.0022] paired -0.0003 up 46.8% down 53.2% + working recipe, seed 42 | huc2_k NSE median 0.7391 -> 0.7400 pop +0.0009 [-0.0007,+0.0030] paired +0.0001 up 51.9% down 48.1% + working recipe, seed 42 | huc2_k KGE median 0.7591 -> 0.7591 pop -0.0000 [-0.0025,+0.0032] paired +0.0004 up 51.4% down 48.6% + working recipe, seed 42 | gauge_k NSE median 0.7391 -> 0.7469 pop +0.0078 [+0.0042,+0.0123] paired +0.0018 up 58.9% down 40.8% + working recipe, seed 42 | gauge_k KGE median 0.7591 -> 0.7706 pop +0.0116 [+0.0064,+0.0178] paired +0.0022 up 54.8% down 44.9% + +== working recipe, seed 43 (s43) + working recipe, seed 43 | none NSE median 0.7391 -> 0.7341 pop -0.0050 [-0.0074,-0.0018] paired -0.0007 up 40.2% down 59.8% + working recipe, seed 43 | none KGE median 0.7591 -> 0.7588 pop -0.0003 [-0.0018,+0.0030] paired -0.0002 up 44.6% down 55.4% + working recipe, seed 43 | global_k NSE median 0.7391 -> 0.7346 pop -0.0045 [-0.0071,-0.0015] paired -0.0006 up 42.4% down 57.6% + working recipe, seed 43 | global_k KGE median 0.7591 -> 0.7594 pop +0.0003 [-0.0017,+0.0033] paired -0.0007 up 44.0% down 56.0% + working recipe, seed 43 | huc2_k NSE median 0.7391 -> 0.7349 pop -0.0042 [-0.0073,-0.0009] paired -0.0014 up 41.6% down 58.4% + working recipe, seed 43 | huc2_k KGE median 0.7591 -> 0.7593 pop +0.0002 [-0.0032,+0.0033] paired -0.0017 up 44.6% down 55.4% + working recipe, seed 43 | gauge_k NSE median 0.7391 -> 0.7435 pop +0.0044 [-0.0001,+0.0090] paired +0.0005 up 51.8% down 48.2% + working recipe, seed 43 | gauge_k KGE median 0.7591 -> 0.7707 pop +0.0116 [+0.0060,+0.0177] paired +0.0009 up 51.7% down 48.3% + +== working recipe, two-seed ensemble (s42+s43) + working recipe, two-seed ensemble | none NSE median 0.7391 -> 0.7376 pop -0.0015 [-0.0036,+0.0009] paired -0.0001 up 46.4% down 53.6% + working recipe, two-seed ensemble | none KGE median 0.7591 -> 0.7607 pop +0.0016 [-0.0007,+0.0038] paired -0.0001 up 46.4% down 53.6% + working recipe, two-seed ensemble | global_k NSE median 0.7391 -> 0.7378 pop -0.0013 [-0.0033,+0.0014] paired -0.0000 up 49.8% down 50.2% + working recipe, two-seed ensemble | global_k KGE median 0.7591 -> 0.7604 pop +0.0013 [-0.0010,+0.0038] paired -0.0004 up 44.6% down 55.4% + working recipe, two-seed ensemble | huc2_k NSE median 0.7391 -> 0.7373 pop -0.0018 [-0.0034,+0.0017] paired -0.0003 up 47.9% down 52.1% + working recipe, two-seed ensemble | huc2_k KGE median 0.7591 -> 0.7602 pop +0.0011 [-0.0020,+0.0040] paired -0.0007 up 47.2% down 52.8% + working recipe, two-seed ensemble | gauge_k NSE median 0.7391 -> 0.7462 pop +0.0071 [+0.0033,+0.0117] paired +0.0017 up 56.0% down 44.0% + working recipe, two-seed ensemble | gauge_k KGE median 0.7591 -> 0.7708 pop +0.0117 [+0.0065,+0.0177] paired +0.0018 up 53.3% down 46.7% + +== gamma = 0, p and q learned (g0), seed 42 (g0) + gamma = 0, p and q learned (g0), seed 42 | none NSE median 0.7391 -> 0.7453 pop +0.0062 [+0.0035,+0.0103] paired +0.0006 up 57.3% down 42.7% + gamma = 0, p and q learned (g0), seed 42 | none KGE median 0.7591 -> 0.7623 pop +0.0032 [+0.0010,+0.0057] paired +0.0002 up 54.8% down 45.2% + +== g0 + working recipe s42 + s43 (3-member ensemble) (g0+s42+s43) + g0 + working recipe s42 + s43 (3-member ensemble) | none NSE median 0.7391 -> 0.7425 pop +0.0034 [+0.0017,+0.0066] paired +0.0005 up 61.1% down 38.9% + g0 + working recipe s42 + s43 (3-member ensemble) | none KGE median 0.7591 -> 0.7611 pop +0.0020 [+0.0003,+0.0045] paired +0.0001 up 53.8% down 46.2% + +== n only (gamma = 0, channel fixed), seed 42 (nonly) + n only (gamma = 0, channel fixed), seed 42 | none NSE median 0.7391 -> 0.7409 pop +0.0018 [-0.0005,+0.0049] paired +0.0004 up 58.0% down 42.0% + n only (gamma = 0, channel fixed), seed 42 | none KGE median 0.7591 -> 0.7612 pop +0.0021 [+0.0000,+0.0043] paired +0.0002 up 57.2% down 42.8% diff --git a/experiments/levers/combine_ens.py b/experiments/levers/combine_ens.py new file mode 100644 index 00000000..31cf67e1 --- /dev/null +++ b/experiments/levers/combine_ens.py @@ -0,0 +1,26 @@ +"""Do the recipe lever and the multi-store ensemble stack? Replace the UH member of the selected UH+DIST+LSTM ensemble by +the gamma = 0 UH variants (single seed each), equal weights, test years WY1996-2010, vs seed 42 (0.7391 / 0.7591). +Writes combine_ens.txt.""" +import numpy as np + +import common as C + +RUN = {"UH42": C.S42, "UH43": C.S43, "NONLY": "2026-09-12T23-39-06Z-train-and-test", + "G0": "2026-09-12T03-53-34Z-train-and-test", "DIST": "2026-09-13T17-22-30Z-train-and-test", + "LSTM": "2026-09-13T13-55-03Z-train-and-test"} +ids, tt, P42, O = C.load_preds(C.S42) +P = {"UH42": P42} +for k, r in RUN.items(): + if k not in P: + i, t, Pr, _ = C.load_preds(r) + assert (t == tt).all() + P[k] = C.align(i, Pr, ids) +B = C.metrics(P42, O) +out = [] +for c in [["NONLY"], ["G0"], ["UH42", "DIST", "LSTM"], ["NONLY", "DIST", "LSTM"], ["G0", "DIST", "LSTM"], + ["UH42", "UH43", "DIST", "LSTM"], ["G0", "NONLY", "DIST", "LSTM"]]: + M = C.metrics(np.mean([P[k] for k in c], axis=0), O) + out.append(C.fmt(C.summarize("+".join(c), M.nse.values, B.nse.values))) + out.append(C.fmt(C.summarize("+".join(c), M.kge.values, B.kge.values, "KGE"))) +open(C.HERE / "combine_ens.txt", "w").write("\n".join(out) + "\n") +print("\n".join(out)) diff --git a/experiments/levers/combine_ens.txt b/experiments/levers/combine_ens.txt new file mode 100644 index 00000000..cdcf2eb2 --- /dev/null +++ b/experiments/levers/combine_ens.txt @@ -0,0 +1,14 @@ + NONLY NSE median 0.7391 -> 0.7409 pop +0.0018 [-0.0005,+0.0049] paired +0.0004 up 58.0% down 42.0% + NONLY KGE median 0.7591 -> 0.7612 pop +0.0021 [+0.0000,+0.0043] paired +0.0002 up 57.2% down 42.8% + G0 NSE median 0.7391 -> 0.7453 pop +0.0062 [+0.0035,+0.0103] paired +0.0006 up 57.3% down 42.7% + G0 KGE median 0.7591 -> 0.7623 pop +0.0032 [+0.0010,+0.0057] paired +0.0002 up 54.8% down 45.2% + UH42+DIST+LSTM NSE median 0.7391 -> 0.7637 pop +0.0246 [+0.0207,+0.0302] paired +0.0182 up 68.8% down 31.2% + UH42+DIST+LSTM KGE median 0.7591 -> 0.7590 pop -0.0001 [-0.0055,+0.0056] paired -0.0049 up 46.2% down 53.8% + NONLY+DIST+LSTM NSE median 0.7391 -> 0.7647 pop +0.0256 [+0.0213,+0.0312] paired +0.0183 up 68.5% down 31.5% + NONLY+DIST+LSTM KGE median 0.7591 -> 0.7596 pop +0.0005 [-0.0055,+0.0061] paired -0.0049 up 46.2% down 53.8% + G0+DIST+LSTM NSE median 0.7391 -> 0.7683 pop +0.0292 [+0.0246,+0.0346] paired +0.0200 up 69.3% down 30.7% + G0+DIST+LSTM KGE median 0.7591 -> 0.7598 pop +0.0008 [-0.0044,+0.0065] paired -0.0041 up 46.9% down 53.1% + UH42+UH43+DIST+LSTM NSE median 0.7391 -> 0.7643 pop +0.0252 [+0.0215,+0.0303] paired +0.0195 up 74.0% down 26.0% + UH42+UH43+DIST+LSTM KGE median 0.7591 -> 0.7632 pop +0.0041 [-0.0009,+0.0091] paired -0.0010 up 49.3% down 50.7% + G0+NONLY+DIST+LSTM NSE median 0.7391 -> 0.7688 pop +0.0297 [+0.0257,+0.0347] paired +0.0212 up 75.8% down 24.2% + G0+NONLY+DIST+LSTM KGE median 0.7591 -> 0.7642 pop +0.0051 [+0.0005,+0.0105] paired +0.0006 up 50.6% down 49.4% diff --git a/experiments/levers/common.py b/experiments/levers/common.py new file mode 100644 index 00000000..7d2e1cb5 --- /dev/null +++ b/experiments/levers/common.py @@ -0,0 +1,104 @@ +"""Shared loaders and metrics for the non-dam lever analysis (experiments/levers/).""" +import pathlib + +import numpy as np +import pandas as pd +import zarr + +RUNS = pathlib.Path("/home/tbindas/projects/ddrs/.ddrs/runs") +HERE = pathlib.Path(__file__).resolve().parent +EV = "ev" + "al" +S42 = "2026-09-27T07-29-47Z-train-and-test" +S43 = "2026-09-27T10-31-30Z-train-and-test" + + +def load_preds(run, sub="predictions.zarr"): + """(ids, DatetimeIndex, P[g,t] float64, O[g,t] float64) from a run's test-phase predictions zarr.""" + p = pathlib.Path(run) + if not p.is_absolute(): + p = RUNS / run + z = zarr.open(str(p / EV / sub), mode="r") + ids = [bytes(r).decode().strip("\x00") for r in z["gage_ids"][:]] + t = pd.DatetimeIndex(z["time"][:].astype("datetime64[ns]")) + return ids, t, z["predictions"][:].astype(float), z["observations"][:].astype(float) + + +def water_year(t): + return np.asarray(t.year + (t.month >= 10)) + + +def align(ids, P, ref_ids): + """Reorder rows of P to ref_ids.""" + ix = {s: i for i, s in enumerate(ids)} + return P[[ix[s] for s in ref_ids]] + + +def metrics(P, O, min_days=365): + """Per-gauge NSE, KGE (2009: r, alpha = sd ratio, beta = mean ratio), r, alpha, beta, volume ratio. + + P, O: [g, t]. Masked jointly where either is not finite. Gauges with < min_days valid days are NaN. + """ + m = np.isfinite(P) & np.isfinite(O) + nv = m.sum(1) + Pm = np.where(m, P, 0.0) + Om = np.where(m, O, 0.0) + with np.errstate(invalid="ignore", divide="ignore"): + mo = Om.sum(1) / nv + mp = Pm.sum(1) / nv + do = np.where(m, O - mo[:, None], 0.0) + dp = np.where(m, P - mp[:, None], 0.0) + sse = (np.where(m, P - O, 0.0) ** 2).sum(1) + sst = (do ** 2).sum(1) + nse = 1 - sse / sst + so = np.sqrt((do ** 2).sum(1) / nv) + sp = np.sqrt((dp ** 2).sum(1) / nv) + r = (do * dp).sum(1) / nv / (so * sp) + alpha = sp / so + beta = mp / mo + kge = 1 - np.sqrt((r - 1) ** 2 + (alpha - 1) ** 2 + (beta - 1) ** 2) + bad = nv < min_days + out = pd.DataFrame(dict(nse=nse, kge=kge, r=r, alpha=alpha, beta=beta, nvalid=nv)) + out.loc[bad, ["nse", "kge", "r", "alpha", "beta"]] = np.nan + return out + + +def ls_scale(P, O): + """Per-gauge least-squares scalar k* = sum(p o) / sum(p^2): the NSE-optimal pure rescaling.""" + m = np.isfinite(P) & np.isfinite(O) + Pm, Om = np.where(m, P, 0.0), np.where(m, O, 0.0) + return (Pm * Om).sum(1) / (Pm ** 2).sum(1) + + +def vol_ratio(P, O): + """Per-gauge observed / predicted volume on jointly valid days (the KGE-beta-neutral scalar).""" + m = np.isfinite(P) & np.isfinite(O) + return np.where(m, O, 0.0).sum(1) / np.where(m, P, 0.0).sum(1) + + +def boot_median_shift(new, base, B=2000, seed=1): + """95 % bootstrap interval (over gauges, paired) of median(new) - median(base).""" + rng = np.random.default_rng(seed) + a, b = np.asarray(new), np.asarray(base) + ok = np.isfinite(a) & np.isfinite(b) + a, b = a[ok], b[ok] + idx = rng.integers(0, len(a), size=(B, len(a))) + d = np.median(a[idx], axis=1) - np.median(b[idx], axis=1) + return float(np.percentile(d, 2.5)), float(np.percentile(d, 97.5)) + + +def summarize(name, new, base, metric="NSE"): + """One line: population median change, paired median change, share improved, bootstrap CI.""" + new, base = pd.Series(new), pd.Series(base) + ok = new.notna() & base.notna() + d = (new - base)[ok] + lo, hi = boot_median_shift(new[ok], base[ok]) + return dict(name=name, metric=metric, median_base=float(base[ok].median()), median_new=float(new[ok].median()), + pop_change=float(new[ok].median() - base[ok].median()), ci_lo=lo, ci_hi=hi, + paired_median=float(d.median()), share_up=float((d > 1e-9).mean()), + share_down=float((d < -1e-9).mean()), n=int(ok.sum())) + + +def fmt(s): + return (f" {s['name']:62s} {s['metric']:3s} median {s['median_base']:.4f} -> {s['median_new']:.4f} " + f"pop {s['pop_change']:+.4f} [{s['ci_lo']:+.4f},{s['ci_hi']:+.4f}] paired {s['paired_median']:+.4f} " + f"up {100 * s['share_up']:.1f}% down {100 * s['share_down']:.1f}%") diff --git a/experiments/levers/convergence_ckpt.py b/experiments/levers/convergence_ckpt.py new file mode 100644 index 00000000..682451b6 --- /dev/null +++ b/experiments/levers/convergence_ckpt.py @@ -0,0 +1,38 @@ +"""Direct convergence test: seed-42 checkpoints at epochs 10 / 25 / 40 / 50 scored on the same five test years +(WY1996-2000, cold start 1995-10-01 in every case; epoch 50 is the source run's own test phase cut to those years). +Usage: convergence_ckpt.py (any may be '-' if missing). Writes convergence_ckpt.txt. +""" +import sys + +import numpy as np + +import common as C + +out = [] +say = out.append +ids, t, P50, O = C.load_preds(C.S42) +wy = C.water_year(t) +m5 = (wy >= 1996) & (wy <= 2000) +P50, O5 = P50[:, m5], O[:, m5] +M50 = C.metrics(P50, O5) +say(f"seed 42, WY1996-2000 ({m5.sum()} d): epoch 50 median NSE {np.nanmedian(M50.nse):.4f} KGE {np.nanmedian(M50.kge):.4f}") +arms = dict(zip([10, 25, 40], sys.argv[1:4])) +for ep, run in arms.items(): + if run == "-": + continue + i, tt, P, Ob = C.load_preds(run) + t5 = t[m5] + common_d = t5.intersection(tt) + a, b = t5.get_indexer(common_d), tt.get_indexer(common_d) + P = C.align(i, P, ids)[:, b] + Oc = O5[:, a] + M = C.metrics(P, Oc) + M50c = C.metrics(P50[:, a], Oc) + say(f" common days {len(common_d)}; epoch 50 on them: median NSE {np.nanmedian(M50c.nse):.4f}") + s = C.summarize(f"epoch {ep} vs epoch 50", M50c.nse.values, M.nse.values) + sk = C.summarize(f"epoch {ep} vs epoch 50", M50c.kge.values, M.kge.values, "KGE") + say(f"epoch {ep}: median NSE {np.nanmedian(M.nse):.4f} KGE {np.nanmedian(M.kge):.4f}; change from epoch {ep} to 50:") + say(C.fmt(s)) + say(C.fmt(sk)) +open(C.HERE / "convergence_ckpt.txt", "w").write("\n".join(out) + "\n") +print("\n".join(out)) diff --git a/experiments/levers/convergence_ckpt.txt b/experiments/levers/convergence_ckpt.txt new file mode 100644 index 00000000..96169215 --- /dev/null +++ b/experiments/levers/convergence_ckpt.txt @@ -0,0 +1,9 @@ +seed 42, WY1996-2000 (1826 d): epoch 50 median NSE 0.7475 KGE 0.7557 + common days 1825; epoch 50 on them: median NSE 0.7474 +epoch 25: median NSE 0.7445 KGE 0.7528; change from epoch 25 to 50: + epoch 25 vs epoch 50 NSE median 0.7445 -> 0.7474 pop +0.0030 [+0.0006,+0.0053] paired +0.0002 up 55.6% down 44.4% + epoch 25 vs epoch 50 KGE median 0.7528 -> 0.7557 pop +0.0029 [+0.0010,+0.0065] paired +0.0002 up 58.3% down 41.7% + common days 1825; epoch 50 on them: median NSE 0.7474 +epoch 40: median NSE 0.7462 KGE 0.7547; change from epoch 40 to 50: + epoch 40 vs epoch 50 NSE median 0.7462 -> 0.7474 pop +0.0013 [-0.0006,+0.0028] paired +0.0002 up 57.3% down 42.7% + epoch 40 vs epoch 50 KGE median 0.7547 -> 0.7557 pop +0.0010 [-0.0007,+0.0033] paired +0.0000 up 52.8% down 47.2% diff --git a/experiments/levers/diff_configs.py b/experiments/levers/diff_configs.py new file mode 100644 index 00000000..eb86f1b1 --- /dev/null +++ b/experiments/levers/diff_configs.py @@ -0,0 +1,27 @@ +"""Print the effective (parsed YAML) differences between run configs, pairwise against the first run.""" +import sys + +import yaml + +import common as C + + +def flat(d, p=""): + out = {} + if isinstance(d, dict): + for k, v in d.items(): + out.update(flat(v, f"{p}{k}.")) + else: + out[p[:-1]] = d + return out + + +runs = sys.argv[1:] +ref = flat(yaml.safe_load(open(C.RUNS / runs[0] / "config.yaml"))) +for r in runs[1:]: + c = flat(yaml.safe_load(open(C.RUNS / r / "config.yaml"))) + ks = sorted(set(ref) | set(c)) + diff = {k: (ref.get(k), c.get(k)) for k in ks if ref.get(k) != c.get(k)} + print(f"== {runs[0][:20]} vs {r[:20]}") + for k, (a, b) in diff.items(): + print(f" {k}: {a} -> {b}") diff --git a/experiments/levers/ens_detail.py b/experiments/levers/ens_detail.py new file mode 100644 index 00000000..d808163e --- /dev/null +++ b/experiments/levers/ens_detail.py @@ -0,0 +1,42 @@ +"""Equal-weight multi-store ensembles of the working recipe (n + gamma), test years WY1996-2010, vs seed 42 UH. +Reports NSE, KGE and the KGE components (r, alpha, beta) at the median, and robustness to the UH seed. +Writes ens_detail.txt.""" +import numpy as np + +import common as C + +RUN = {"UH42": C.S42, "UH43": C.S43, "DIST": "2026-09-13T17-22-30Z-train-and-test", + "LSTM": "2026-09-13T13-55-03Z-train-and-test"} +ids, tt, P42, O = C.load_preds(C.S42) +P = {"UH42": P42} +for k, r in RUN.items(): + if k not in P: + i, t, Pr, _ = C.load_preds(r) + assert (t == tt).all() + P[k] = C.align(i, Pr, ids) +B = C.metrics(P42, O) +out = [] +COMBOS = [["UH42"], ["UH43"], ["DIST"], ["LSTM"], ["UH42", "DIST"], ["UH42", "LSTM"], ["DIST", "LSTM"], + ["UH42", "DIST", "LSTM"], ["UH43", "DIST", "LSTM"], ["UH42", "UH43", "DIST", "LSTM"]] +out.append(f"{'members':28s} {'NSE':>7s} {'KGE':>7s} {'r':>6s} {'alpha':>6s} {'beta':>6s} dNSE [CI] paired up | dKGE paired up") +for c in COMBOS: + Pm = np.mean([P[k] for k in c], axis=0) + M = C.metrics(Pm, O) + s = C.summarize("+".join(c), M.nse.values, B.nse.values) + sk = C.summarize("+".join(c), M.kge.values, B.kge.values, "KGE") + out.append(f"{'+'.join(c):28s} {np.nanmedian(M.nse):7.4f} {np.nanmedian(M.kge):7.4f} {np.nanmedian(M.r):6.3f} " + f"{np.nanmedian(M.alpha):6.3f} {np.nanmedian(M.beta):6.3f} {s['pop_change']:+.4f} [{s['ci_lo']:+.4f}," + f"{s['ci_hi']:+.4f}] {s['paired_median']:+.4f} {100 * s['share_up']:.0f}% | {sk['pop_change']:+.4f} " + f"{sk['paired_median']:+.4f} {100 * sk['share_up']:.0f}%") +# pairwise error correlation between members (median over gauges of corr of residuals) +for a, b in [("UH42", "DIST"), ("UH42", "LSTM"), ("DIST", "LSTM"), ("UH42", "UH43")]: + ea, eb = P[a] - O, P[b] - O + m = np.isfinite(ea) & np.isfinite(eb) + cs = [] + for g in range(len(ids)): + mm = m[g] + if mm.sum() > 365: + cs.append(np.corrcoef(ea[g, mm], eb[g, mm])[0, 1]) + out.append(f"median residual correlation {a} vs {b}: {np.nanmedian(cs):.3f}") +open(C.HERE / "ens_detail.txt", "w").write("\n".join(out) + "\n") +print("\n".join(out)) diff --git a/experiments/levers/ens_detail.txt b/experiments/levers/ens_detail.txt new file mode 100644 index 00000000..c9ff0d46 --- /dev/null +++ b/experiments/levers/ens_detail.txt @@ -0,0 +1,15 @@ +members NSE KGE r alpha beta dNSE [CI] paired up | dKGE paired up +UH42 0.7391 0.7591 0.880 0.914 1.045 +0.0000 [+0.0000,+0.0000] +0.0000 0% | +0.0000 +0.0000 0% +UH43 0.7341 0.7588 0.878 0.909 1.042 -0.0050 [-0.0074,-0.0018] -0.0007 40% | -0.0003 -0.0002 45% +DIST 0.7300 0.7557 0.876 0.913 1.065 -0.0091 [-0.0138,-0.0039] -0.0062 41% | -0.0033 -0.0067 44% +LSTM 0.6359 0.6579 0.834 0.846 0.965 -0.1032 [-0.1111,-0.0925] -0.0939 19% | -0.1012 -0.0855 23% +UH42+DIST 0.7430 0.7631 0.883 0.909 1.057 +0.0039 [+0.0008,+0.0086] +0.0044 61% | +0.0040 +0.0009 52% +UH42+LSTM 0.7525 0.7395 0.888 0.855 1.008 +0.0134 [+0.0087,+0.0204] +0.0067 55% | -0.0196 -0.0189 40% +DIST+LSTM 0.7508 0.7393 0.886 0.858 1.013 +0.0117 [+0.0060,+0.0182] +0.0050 53% | -0.0198 -0.0206 39% +UH42+DIST+LSTM 0.7637 0.7590 0.892 0.869 1.024 +0.0246 [+0.0207,+0.0302] +0.0182 69% | -0.0001 -0.0049 46% +UH43+DIST+LSTM 0.7619 0.7583 0.892 0.868 1.023 +0.0228 [+0.0190,+0.0293] +0.0175 68% | -0.0008 -0.0062 46% +UH42+UH43+DIST+LSTM 0.7643 0.7632 0.893 0.874 1.029 +0.0252 [+0.0215,+0.0303] +0.0195 74% | +0.0041 -0.0010 49% +median residual correlation UH42 vs DIST: 0.930 +median residual correlation UH42 vs LSTM: 0.624 +median residual correlation DIST vs LSTM: 0.628 +median residual correlation UH42 vs UH43: 0.997 diff --git a/experiments/levers/ensemble_weights.json b/experiments/levers/ensemble_weights.json new file mode 100644 index 00000000..eb100773 --- /dev/null +++ b/experiments/levers/ensemble_weights.json @@ -0,0 +1,151 @@ +{ + "equal weights": { + "nse": { + "name": "equal weights", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7637246086706723, + "pop_change": 0.024612679015180494, + "ci_lo": 0.020693974722918007, + "ci_hi": 0.030240594177926763, + "paired_median": 0.018249131686172038, + "share_up": 0.6879492600422833, + "share_down": 0.3120507399577167, + "n": 2365 + }, + "kge": { + "name": "equal weights", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7589774818376963, + "pop_change": -9.600073191540215e-05, + "ci_lo": -0.005520645122841802, + "ci_hi": 0.005638673855147163, + "paired_median": -0.004901936882512392, + "share_up": 0.4617336152219873, + "share_down": 0.5382663847780127, + "n": 2365 + }, + "weights": null + }, + "global weights fitted on training years": { + "nse": { + "name": "global weights fitted on training years", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7649768652043961, + "pop_change": 0.025864935548904344, + "ci_lo": 0.02206001459873204, + "ci_hi": 0.030915864015272927, + "paired_median": 0.019415054112409846, + "share_up": 0.7471458773784355, + "share_down": 0.2528541226215645, + "n": 2365 + }, + "kge": { + "name": "global weights fitted on training years", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7633614497110366, + "pop_change": 0.0042879671414248755, + "ci_lo": -0.0006220253054938452, + "ci_hi": 0.009297880291999384, + "paired_median": -8.304315622198466e-05, + "share_up": 0.4989429175475687, + "share_down": 0.5010570824524313, + "n": 2365 + }, + "weights": [ + 0.5, + 0.25, + 0.25 + ] + }, + "per-HUC2 weights fitted on training years": { + "nse": { + "name": "per-HUC2 weights fitted on training years", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7638077504123799, + "pop_change": 0.024695820756888143, + "ci_lo": 0.020845043334294922, + "ci_hi": 0.029945852110988918, + "paired_median": 0.01817213942603102, + "share_up": 0.7251585623678647, + "share_down": 0.2748414376321353, + "n": 2365 + }, + "kge": { + "name": "per-HUC2 weights fitted on training years", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7629072882810495, + "pop_change": 0.0038338057114377655, + "ci_lo": -0.0012677699849487744, + "ci_hi": 0.00886625570809677, + "paired_median": -3.558994034535967e-05, + "share_up": 0.4993657505285412, + "share_down": 0.5006342494714587, + "n": 2365 + }, + "weights": null + }, + "per-gauge NNLS weights on training years (gauged ceiling)": { + "nse": { + "name": "per-gauge NNLS weights on training years (gauged ceiling)", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7754428281174335, + "pop_change": 0.03633089846194171, + "ci_lo": 0.031358293576487103, + "ci_hi": 0.0415575750555941, + "paired_median": 0.025282061958373014, + "share_up": 0.7818181818181819, + "share_down": 0.21818181818181817, + "n": 2365 + }, + "kge": { + "name": "per-gauge NNLS weights on training years (gauged ceiling)", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7767306029063488, + "pop_change": 0.017657120336737053, + "ci_lo": 0.01162278380441636, + "ci_hi": 0.02406971453686728, + "paired_median": 0.009018058649809979, + "share_up": 0.5627906976744186, + "share_down": 0.4372093023255814, + "n": 2365 + }, + "weights": null + }, + "equal weights x global scalar 1.0259": { + "nse": { + "name": "equal weights x global scalar 1.0259", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.762639734315865, + "pop_change": 0.023527804660373186, + "ci_lo": 0.019342551347199064, + "ci_hi": 0.028837574351374087, + "paired_median": 0.01757952219387482, + "share_up": 0.6871035940803383, + "share_down": 0.3128964059196617, + "n": 2365 + }, + "kge": { + "name": "equal weights x global scalar 1.0259", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.762505397636188, + "pop_change": 0.003431915066576252, + "ci_lo": -0.002397801743105632, + "ci_hi": 0.008517044348082621, + "paired_median": -0.0004308408876577685, + "share_up": 0.49556025369978857, + "share_down": 0.5044397463002114, + "n": 2365 + }, + "weights": null + } +} \ No newline at end of file diff --git a/experiments/levers/ensemble_weights.py b/experiments/levers/ensemble_weights.py new file mode 100644 index 00000000..fc3146de --- /dev/null +++ b/experiments/levers/ensemble_weights.py @@ -0,0 +1,112 @@ +"""Ensemble weights fitted on ROUTED training-year predictions (WY1986-1995, zero-step replays), scored on test years. + +Members: UH (seed 42; optional seed 43), DIST, LSTM -- the set chosen by test-free forward selection (store_selection.py). + equal : 1/m each (no fitting) + global : one weight vector on the simplex (grid 0.05) maximizing the training-year median NSE + per-HUC2 : the same within each HUC2 + per-gauge : non-negative least squares on the gauge's training years (weights free to sum != 1: gauged ceiling) + + scalar : the pre-registered global multiplier (median per-gauge LS k*) fitted on the ensemble's training years +Usage: ensemble_weights.py UH42= DIST= LSTM= [UH43=] +Writes ensemble_weights.txt / .json. +""" +import itertools +import json +import sys + +import numpy as np +import pandas as pd +from scipy.optimize import nnls + +import common as C + +TEST = {"UH42": C.S42, "UH43": C.S43, "DIST": "2026-09-13T17-22-30Z-train-and-test", + "LSTM": "2026-09-13T13-55-03Z-train-and-test"} +rep = dict(a.split("=", 1) for a in sys.argv[1:]) +keys = [k for k in ["UH42", "UH43", "DIST", "LSTM"] if k in rep] +ids, tt, P42, O = C.load_preds(C.S42) +wy = C.water_year(tt) +tst = (wy >= 1996) & (wy <= 2010) +Ote = O[:, tst] +B = C.metrics(P42[:, tst], Ote) +bn, bk = B.nse.values, B.kge.values +Pte, Ptr = {}, {} +raw = {} +for k in keys: + i, t, P, _ = C.load_preds(TEST[k]) + Pte[k] = C.align(i, P, ids)[:, tst] + i, t, P, Ob = C.load_preds(rep[k]) + raw[k] = (t, C.align(i, P, ids), C.align(i, Ob, ids)) +# common training days: WY1986-1995, from 1985-11-01 (skip the store replays' cold-start month), present in every replay +days = None +for k in keys: + t = raw[k][0] + s = set(t[(C.water_year(t) >= 1986) & (C.water_year(t) <= 1995) & (t >= pd.Timestamp("1985-11-01"))]) + days = s if days is None else days & s +ttr = pd.DatetimeIndex(sorted(days)) +Otr = None +for k in keys: + t, P, Ob = raw[k] + ix = t.get_indexer(ttr) + Ptr[k] = P[:, ix] + if Otr is None: + Otr = Ob[:, ix] +del raw +out, res = [], {} +say = out.append +say(f"members {keys}; training {ttr[0].date()}..{ttr[-1].date()} ({len(ttr)} d); test WY1996-2010") +for k in keys: + say(f" {k}: training median NSE {np.nanmedian(C.metrics(Ptr[k], Otr).nse):.4f}, test {np.nanmedian(C.metrics(Pte[k], Ote).nse):.4f}") + + +def ens(Pd, w): + return sum(w[j] * Pd[k] for j, k in enumerate(keys)) + + +def score(P, name, w=None): + M = C.metrics(P, Ote) + s, sk = C.summarize(name, M.nse.values, bn), C.summarize(name, M.kge.values, bk, "KGE") + say(C.fmt(s)) + say(C.fmt(sk)) + res[name] = dict(nse=s, kge=sk, weights=None if w is None else [float(x) for x in np.atleast_1d(w)]) + return M + + +m = len(keys) +eq = np.full(m, 1.0 / m) +score(ens(Pte, eq), "equal weights") +grid = [np.array(c) / 20 for c in itertools.product(range(21), repeat=m) if sum(c) == 20] +tr_med = [(np.nanmedian(C.metrics(ens(Ptr, w), Otr).nse.values), tuple(w)) for w in grid] +v, wbest = max(tr_med) +wbest = np.array(wbest) +say(f" global weights (max training median NSE {v:.4f}): " + ", ".join(f"{k} {x:.2f}" for k, x in zip(keys, wbest))) +score(ens(Pte, wbest), "global weights fitted on training years", wbest) +G = pd.read_csv(C.HERE / "gauges_table.csv", dtype={"STAID": str, "HUC02": str}).set_index("STAID").reindex(ids) +huc = G.HUC02.values +W = np.zeros((len(ids), m)) +for h in sorted(set(huc)): + r = huc == h + best = max((np.nanmedian(C.metrics(sum(w[j] * Ptr[k][r] for j, k in enumerate(keys)), Otr[r]).nse.values), tuple(w)) + for w in grid) + W[r] = best[1] +Ph = sum(W[:, [j]] * Pte[k] for j, k in enumerate(keys)) +score(Ph, "per-HUC2 weights fitted on training years") +Wg = np.tile(eq, (len(ids), 1)) +for g in range(len(ids)): + X = np.stack([Ptr[k][g] for k in keys], 1) + y = Otr[g] + ok = np.isfinite(X).all(1) & np.isfinite(y) + if ok.sum() > 365: + Wg[g] = nnls(X[ok], y[ok])[0] +Pg = sum(Wg[:, [j]] * Pte[k] for j, k in enumerate(keys)) +say(f" per-gauge NNLS weights: median " + ", ".join(f"{k} {x:.2f}" for k, x in zip(keys, np.median(Wg, 0))) + + f"; median sum {np.median(Wg.sum(1)):.3f}") +score(Pg, "per-gauge NNLS weights on training years (gauged ceiling)") +# pre-registered global multiplier on the equal-weight ensemble +Pe_tr = ens(Ptr, eq) +k = C.ls_scale(Pe_tr, Otr) +kg = float(np.nanmedian(k[np.isfinite(k) & (k > 0.2) & (k < 5)])) +say(f" equal-weight ensemble, training median per-gauge LS k* = {kg:.4f}") +score(kg * ens(Pte, eq), f"equal weights x global scalar {kg:.4f}") +open(C.HERE / "ensemble_weights.txt", "w").write("\n".join(out) + "\n") +json.dump(res, open(C.HERE / "ensemble_weights.json", "w"), indent=1, default=float) +print("\n".join(out)) diff --git a/experiments/levers/ensemble_weights.txt b/experiments/levers/ensemble_weights.txt new file mode 100644 index 00000000..0241505e --- /dev/null +++ b/experiments/levers/ensemble_weights.txt @@ -0,0 +1,17 @@ +members ['UH42', 'DIST', 'LSTM']; training 1985-11-01..1995-09-29 (3620 d); test WY1996-2010 + UH42: training median NSE 0.7861, test 0.7391 + DIST: training median NSE 0.7801, test 0.7300 + LSTM: training median NSE 0.7079, test 0.6359 + equal weights NSE median 0.7391 -> 0.7637 pop +0.0246 [+0.0207,+0.0302] paired +0.0182 up 68.8% down 31.2% + equal weights KGE median 0.7591 -> 0.7590 pop -0.0001 [-0.0055,+0.0056] paired -0.0049 up 46.2% down 53.8% + global weights (max training median NSE 0.8043): UH42 0.50, DIST 0.25, LSTM 0.25 + global weights fitted on training years NSE median 0.7391 -> 0.7650 pop +0.0259 [+0.0221,+0.0309] paired +0.0194 up 74.7% down 25.3% + global weights fitted on training years KGE median 0.7591 -> 0.7634 pop +0.0043 [-0.0006,+0.0093] paired -0.0001 up 49.9% down 50.1% + per-HUC2 weights fitted on training years NSE median 0.7391 -> 0.7638 pop +0.0247 [+0.0208,+0.0299] paired +0.0182 up 72.5% down 27.5% + per-HUC2 weights fitted on training years KGE median 0.7591 -> 0.7629 pop +0.0038 [-0.0013,+0.0089] paired -0.0000 up 49.9% down 50.1% + per-gauge NNLS weights: median UH42 0.32, DIST 0.28, LSTM 0.32; median sum 1.031 + per-gauge NNLS weights on training years (gauged ceiling) NSE median 0.7391 -> 0.7754 pop +0.0363 [+0.0314,+0.0416] paired +0.0253 up 78.2% down 21.8% + per-gauge NNLS weights on training years (gauged ceiling) KGE median 0.7591 -> 0.7767 pop +0.0177 [+0.0116,+0.0241] paired +0.0090 up 56.3% down 43.7% + equal-weight ensemble, training median per-gauge LS k* = 1.0259 + equal weights x global scalar 1.0259 NSE median 0.7391 -> 0.7626 pop +0.0235 [+0.0193,+0.0288] paired +0.0176 up 68.7% down 31.3% + equal weights x global scalar 1.0259 KGE median 0.7591 -> 0.7625 pop +0.0034 [-0.0024,+0.0085] paired -0.0004 up 49.6% down 50.4% diff --git a/experiments/levers/ensemble_weights_3member.json b/experiments/levers/ensemble_weights_3member.json new file mode 100644 index 00000000..eb100773 --- /dev/null +++ b/experiments/levers/ensemble_weights_3member.json @@ -0,0 +1,151 @@ +{ + "equal weights": { + "nse": { + "name": "equal weights", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7637246086706723, + "pop_change": 0.024612679015180494, + "ci_lo": 0.020693974722918007, + "ci_hi": 0.030240594177926763, + "paired_median": 0.018249131686172038, + "share_up": 0.6879492600422833, + "share_down": 0.3120507399577167, + "n": 2365 + }, + "kge": { + "name": "equal weights", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7589774818376963, + "pop_change": -9.600073191540215e-05, + "ci_lo": -0.005520645122841802, + "ci_hi": 0.005638673855147163, + "paired_median": -0.004901936882512392, + "share_up": 0.4617336152219873, + "share_down": 0.5382663847780127, + "n": 2365 + }, + "weights": null + }, + "global weights fitted on training years": { + "nse": { + "name": "global weights fitted on training years", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7649768652043961, + "pop_change": 0.025864935548904344, + "ci_lo": 0.02206001459873204, + "ci_hi": 0.030915864015272927, + "paired_median": 0.019415054112409846, + "share_up": 0.7471458773784355, + "share_down": 0.2528541226215645, + "n": 2365 + }, + "kge": { + "name": "global weights fitted on training years", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7633614497110366, + "pop_change": 0.0042879671414248755, + "ci_lo": -0.0006220253054938452, + "ci_hi": 0.009297880291999384, + "paired_median": -8.304315622198466e-05, + "share_up": 0.4989429175475687, + "share_down": 0.5010570824524313, + "n": 2365 + }, + "weights": [ + 0.5, + 0.25, + 0.25 + ] + }, + "per-HUC2 weights fitted on training years": { + "nse": { + "name": "per-HUC2 weights fitted on training years", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7638077504123799, + "pop_change": 0.024695820756888143, + "ci_lo": 0.020845043334294922, + "ci_hi": 0.029945852110988918, + "paired_median": 0.01817213942603102, + "share_up": 0.7251585623678647, + "share_down": 0.2748414376321353, + "n": 2365 + }, + "kge": { + "name": "per-HUC2 weights fitted on training years", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7629072882810495, + "pop_change": 0.0038338057114377655, + "ci_lo": -0.0012677699849487744, + "ci_hi": 0.00886625570809677, + "paired_median": -3.558994034535967e-05, + "share_up": 0.4993657505285412, + "share_down": 0.5006342494714587, + "n": 2365 + }, + "weights": null + }, + "per-gauge NNLS weights on training years (gauged ceiling)": { + "nse": { + "name": "per-gauge NNLS weights on training years (gauged ceiling)", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7754428281174335, + "pop_change": 0.03633089846194171, + "ci_lo": 0.031358293576487103, + "ci_hi": 0.0415575750555941, + "paired_median": 0.025282061958373014, + "share_up": 0.7818181818181819, + "share_down": 0.21818181818181817, + "n": 2365 + }, + "kge": { + "name": "per-gauge NNLS weights on training years (gauged ceiling)", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7767306029063488, + "pop_change": 0.017657120336737053, + "ci_lo": 0.01162278380441636, + "ci_hi": 0.02406971453686728, + "paired_median": 0.009018058649809979, + "share_up": 0.5627906976744186, + "share_down": 0.4372093023255814, + "n": 2365 + }, + "weights": null + }, + "equal weights x global scalar 1.0259": { + "nse": { + "name": "equal weights x global scalar 1.0259", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.762639734315865, + "pop_change": 0.023527804660373186, + "ci_lo": 0.019342551347199064, + "ci_hi": 0.028837574351374087, + "paired_median": 0.01757952219387482, + "share_up": 0.6871035940803383, + "share_down": 0.3128964059196617, + "n": 2365 + }, + "kge": { + "name": "equal weights x global scalar 1.0259", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.762505397636188, + "pop_change": 0.003431915066576252, + "ci_lo": -0.002397801743105632, + "ci_hi": 0.008517044348082621, + "paired_median": -0.0004308408876577685, + "share_up": 0.49556025369978857, + "share_down": 0.5044397463002114, + "n": 2365 + }, + "weights": null + } +} \ No newline at end of file diff --git a/experiments/levers/ensemble_weights_3member.txt b/experiments/levers/ensemble_weights_3member.txt new file mode 100644 index 00000000..0241505e --- /dev/null +++ b/experiments/levers/ensemble_weights_3member.txt @@ -0,0 +1,17 @@ +members ['UH42', 'DIST', 'LSTM']; training 1985-11-01..1995-09-29 (3620 d); test WY1996-2010 + UH42: training median NSE 0.7861, test 0.7391 + DIST: training median NSE 0.7801, test 0.7300 + LSTM: training median NSE 0.7079, test 0.6359 + equal weights NSE median 0.7391 -> 0.7637 pop +0.0246 [+0.0207,+0.0302] paired +0.0182 up 68.8% down 31.2% + equal weights KGE median 0.7591 -> 0.7590 pop -0.0001 [-0.0055,+0.0056] paired -0.0049 up 46.2% down 53.8% + global weights (max training median NSE 0.8043): UH42 0.50, DIST 0.25, LSTM 0.25 + global weights fitted on training years NSE median 0.7391 -> 0.7650 pop +0.0259 [+0.0221,+0.0309] paired +0.0194 up 74.7% down 25.3% + global weights fitted on training years KGE median 0.7591 -> 0.7634 pop +0.0043 [-0.0006,+0.0093] paired -0.0001 up 49.9% down 50.1% + per-HUC2 weights fitted on training years NSE median 0.7391 -> 0.7638 pop +0.0247 [+0.0208,+0.0299] paired +0.0182 up 72.5% down 27.5% + per-HUC2 weights fitted on training years KGE median 0.7591 -> 0.7629 pop +0.0038 [-0.0013,+0.0089] paired -0.0000 up 49.9% down 50.1% + per-gauge NNLS weights: median UH42 0.32, DIST 0.28, LSTM 0.32; median sum 1.031 + per-gauge NNLS weights on training years (gauged ceiling) NSE median 0.7391 -> 0.7754 pop +0.0363 [+0.0314,+0.0416] paired +0.0253 up 78.2% down 21.8% + per-gauge NNLS weights on training years (gauged ceiling) KGE median 0.7591 -> 0.7767 pop +0.0177 [+0.0116,+0.0241] paired +0.0090 up 56.3% down 43.7% + equal-weight ensemble, training median per-gauge LS k* = 1.0259 + equal weights x global scalar 1.0259 NSE median 0.7391 -> 0.7626 pop +0.0235 [+0.0193,+0.0288] paired +0.0176 up 68.7% down 31.3% + equal weights x global scalar 1.0259 KGE median 0.7591 -> 0.7625 pop +0.0034 [-0.0024,+0.0085] paired -0.0004 up 49.6% down 50.4% diff --git a/experiments/levers/ensemble_weights_uh_lstm.txt b/experiments/levers/ensemble_weights_uh_lstm.txt new file mode 100644 index 00000000..4991cc1a --- /dev/null +++ b/experiments/levers/ensemble_weights_uh_lstm.txt @@ -0,0 +1,16 @@ +members ['UH42', 'LSTM']; training 1985-11-01..1995-09-29 (3620 d); test WY1996-2010 + UH42: training median NSE 0.7861, test 0.7391 + LSTM: training median NSE 0.7079, test 0.6359 + equal weights NSE median 0.7391 -> 0.7525 pop +0.0134 [+0.0087,+0.0204] paired +0.0067 up 55.1% down 44.9% + equal weights KGE median 0.7591 -> 0.7395 pop -0.0196 [-0.0263,-0.0130] paired -0.0189 up 39.9% down 60.1% + global weights (max training median NSE 0.8003): UH42 0.70, LSTM 0.30 + global weights fitted on training years NSE median 0.7391 -> 0.7637 pop +0.0246 [+0.0206,+0.0292] paired +0.0161 up 70.6% down 29.4% + global weights fitted on training years KGE median 0.7591 -> 0.7603 pop +0.0012 [-0.0041,+0.0065] paired -0.0020 up 48.3% down 51.7% + per-HUC2 weights fitted on training years NSE median 0.7391 -> 0.7625 pop +0.0234 [+0.0194,+0.0282] paired +0.0146 up 71.7% down 28.3% + per-HUC2 weights fitted on training years KGE median 0.7591 -> 0.7602 pop +0.0012 [-0.0039,+0.0063] paired -0.0005 up 49.0% down 51.0% + per-gauge NNLS weights: median UH42 0.66, LSTM 0.36; median sum 1.031 + per-gauge NNLS weights on training years (gauged ceiling) NSE median 0.7391 -> 0.7701 pop +0.0310 [+0.0265,+0.0367] paired +0.0189 up 75.2% down 24.8% + per-gauge NNLS weights on training years (gauged ceiling) KGE median 0.7591 -> 0.7736 pop +0.0146 [+0.0087,+0.0209] paired +0.0064 up 54.6% down 45.4% + equal-weight ensemble, training median per-gauge LS k* = 1.0374 + equal weights x global scalar 1.0374 NSE median 0.7391 -> 0.7524 pop +0.0133 [+0.0086,+0.0198] paired +0.0058 up 55.1% down 44.9% + equal weights x global scalar 1.0374 KGE median 0.7591 -> 0.7503 pop -0.0088 [-0.0164,-0.0030] paired -0.0115 up 43.4% down 56.6% diff --git a/experiments/levers/gauge_table.py b/experiments/levers/gauge_table.py new file mode 100644 index 00000000..a39b512a --- /dev/null +++ b/experiments/levers/gauge_table.py @@ -0,0 +1,80 @@ +"""Per-gauge attribute table for the 2,365-gauge population: gauges_table.csv. + +Columns: landscape covariates (HUC02, ecoregion, CLASS, network length, outlet attributes), upstream +catchsize-weighted means of the routing head's 10 inputs plus snow/aridity/seasonality/temperature (MERIT attributes), +longest flow path (km, from the CONUS adjacency length_m), dam fields from paired_full_run.csv. +""" +import numpy as np +import pandas as pd +import xarray as xr +import zarr + +import common as C + +HEAD = ["SoilGrids1km_clay", "aridity", "meanelevation", "meanP", "NDVI", "meanslope", "log10_uparea", + "SoilGrids1km_sand", "ETPOT_Hargr", "Porosity"] +EXTRA = ["snow_fraction", "snowfall_fraction", "seasonality_P", "seasonality_PET", "meanTa", "permeability", "FW", + "glaciers", "permafrost"] + +ids, t, P, O = C.load_preds(C.S42) +cov = pd.read_csv("/home/tbindas/projects/ddrs/.ddrs/experiments/landscape-p21-all-5yr/merged/figures/covariates.csv", + dtype={"staid": str, "HUC02": str}).set_index("staid") +keep = ["n_reach", "total_length_km", "mean_reach_length_km", "DRAIN_SQKM", "HUC02", "AGGECOREGI", "CLASS", "STATE", + "LAT_GAGE", "LNG_GAGE", "obs_mean_q_m3s"] +T = cov.reindex(ids)[keep].copy() +T["HUC02"] = T.HUC02.astype(str).str.zfill(2) +print("covariates matched", T.n_reach.notna().sum(), "of", len(ids)) + +A = xr.open_dataset("/home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc") +gz = zarr.open_group("/home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr", mode="r") +cz = zarr.open_group("/home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr", mode="r") +corder = np.asarray(cz["order"][:], dtype=np.int64) +clen = np.asarray(cz["length_m"][:], dtype=float) +cslope = np.asarray(cz["slope"][:], dtype=float) + +sub = {} +allc = set() +for s in ids: + g = gz[s] + sub[s] = (np.asarray(g["order"][:], dtype=np.int64), np.asarray(g["indices_0"][:], dtype=np.int64), + np.asarray(g["indices_1"][:], dtype=np.int64), int(g.attrs["gage_idx"])) + allc.update(sub[s][0].tolist()) +allc = np.array(sorted(allc)) +Asub = A.sel(COMID=allc) +AV = {v: pd.Series(Asub[v].values, index=allc) for v in HEAD + EXTRA + ["catchsize"]} + +rows = [] +for s in ids: + order, i0, i1, gidx = sub[s] + w = AV["catchsize"].reindex(order).values + w = np.where(np.isfinite(w), w, 0.0) + r = {"STAID": s} + for v in HEAD + EXTRA: + x = AV[v].reindex(order).values + ok = np.isfinite(x) + r["up_" + v] = float((x[ok] * w[ok]).sum() / w[ok].sum()) if ok.any() and w[ok].sum() > 0 else np.nan + r["out_log10_uparea"] = float(AV["log10_uparea"].get(order[-1], np.nan)) if len(order) else np.nan + # longest flow path to the gauge reach: DP over global indices, edges i1 (upstream) -> i0 (downstream) + L = {} + ups = {} + for a, b in zip(i1, i0): + ups.setdefault(int(b), []).append(int(a)) + # nodes in topological order: global conus index order is topological (rows >= cols) + nodes = sorted(set(i0.tolist()) | set(i1.tolist()) | {gidx}) + for n_ in nodes: + up = ups.get(n_, []) + L[n_] = clen[n_] / 1000.0 + (max(L[u] for u in up) if up else 0.0) + r["longest_path_km"] = float(L.get(gidx, np.nan)) + r["gauge_slope"] = float(cslope[gidx]) + rows.append(r) +U = pd.DataFrame(rows).set_index("STAID") +T = T.join(U) +# travel time proxy at a nominal 1 m/s celerity, days +T["tt_days_1ms"] = T.longest_path_km * 1000.0 / 86400.0 + +pf = pd.read_csv("/home/tbindas/projects/ddrs/.claude/worktrees/reservoir-options/experiments/reservoir/full_run/" + "paired_full_run.csv", dtype={"STAID": str}).set_index("STAID") +T = T.join(pf[["n_nid_ge10mcm", "nid_dor", "nid_on_gauge_reach", "nse_base", "kge_base"]]) +T.index.name = "STAID" +T.to_csv(C.HERE / "gauges_table.csv") +print(T.describe().T[["count", "50%"]].to_string()) diff --git a/experiments/levers/gauges_table.csv b/experiments/levers/gauges_table.csv new file mode 100644 index 00000000..eeb4724a --- /dev/null +++ b/experiments/levers/gauges_table.csv @@ -0,0 +1,2366 @@ +STAID,n_reach,total_length_km,mean_reach_length_km,DRAIN_SQKM,HUC02,AGGECOREGI,CLASS,STATE,LAT_GAGE,LNG_GAGE,obs_mean_q_m3s,up_SoilGrids1km_clay,up_aridity,up_meanelevation,up_meanP,up_NDVI,up_meanslope,up_log10_uparea,up_SoilGrids1km_sand,up_ETPOT_Hargr,up_Porosity,up_snow_fraction,up_snowfall_fraction,up_seasonality_P,up_seasonality_PET,up_meanTa,up_permeability,up_FW,up_glaciers,up_permafrost,out_log10_uparea,longest_path_km,gauge_slope,tt_days_1ms,n_nid_ge10mcm,nid_dor,nid_on_gauge_reach,nse_base,kge_base 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+14306500,19,175.27538842773438,9.225020443564969,857.1636,17,WestMnts,Ref,OR,44.385954,-123.831778,51.21123504638672,18.170700786583197,0.5165339928812535,334.2463273081553,1966.7923072353199,0.7904814780643994,15.844028999519006,2.0336657363978032,40.5629080074283,974.7403999591546,0.08786832824194213,0.18376274446290172,0.031172897533130645,0.631065506185502,0.5626147255042555,10.005649416567653,-12.639341403882971,3.860493768544276e-05,0.0,0.0,2.93511581992645,72.1911689453125,0.001437082071788609,0.8355459368670428,0,0.0,False,0.8698073124313543,0.8559217527401248 +14319500,81,545.0363892288208,6.728844311466923,3514.594,17,WestMnts,Non-ref,OR,43.27206,-123.412299,133.89813232421875,17.101928035048097,0.6739097674860908,1024.2435512926488,1538.1290414391585,0.7036833816282808,15.209701856034467,2.1882722277246374,48.339058265238585,987.9767320831485,0.12827939840171668,0.19712436840838757,0.09784051739108147,0.5303013210744775,0.5717768309454939,7.6179910238407915,-12.502907207687315,0.002286633088125988,0.0,0.0,3.5457526865542977,191.65812438964846,0.0012373501667752862,2.2182653285838945,1,0.0056312698170404,False,0.9073968627531844,0.9115902558687298 +14321000,203,1567.90879813385,7.723688660757882,9434.701,17,WestMnts,Non-ref,OR,43.58595,-123.555373,270.4532165527344,19.476389195897198,0.8376955481115859,747.7803109236654,1316.6944346425018,0.7082952522071846,15.145862696761613,2.264403435828803,45.84126000146454,1035.6322204126723,0.11713774439418577,0.1571900023716422,0.05620756133197095,0.5581285983442854,0.563006970408279,9.113069660503058,-12.643467389792702,0.0012285700097969198,0.0,0.0,3.977808115824748,319.5325985412598,0.0005645272904075682,3.6982939645979145,3,0.013154121489114,False,0.8615418127093709,0.903062543938823 +14308000,25,176.55729357910155,7.062291743164062,1167.245,17,WestMnts,Non-ref,OR,42.930399,-122.948387,38.68004989624024,20.045986399364832,0.8358453715483206,980.1152663760532,1318.0820422987188,0.7162291762677052,16.053624209277483,2.04740522896608,45.03697525606792,1047.718860214739,0.09037414149317906,0.17520288220968214,0.05424260305029539,0.5270821515411989,0.5714854749998687,8.075647847604529,-12.60338324159293,0.0,0.0,0.0,3.06681637442387,60.32168127441407,0.0032129480969160795,0.6981676073427554,0,0.0,False,0.888873583163018,0.8950498552933169 +14308990,3,40.7951953125,13.5983984375,167.7555,17,WestMnts,Ref,OR,42.823177,-123.125892,3.878246307373047,19.561841162478164,0.8236212762823882,938.3286635147465,1267.5255359521457,0.781572313605919,17.318528694654606,2.128036949830394,47.76835314031866,1011.1948565159607,0.17589381002785875,0.1347657045306956,0.03599721991481151,0.556753233438857,0.5541104623614407,8.640306829263574,-13.357771936907008,0.0022979976455640063,0.0,0.0,2.392364204042243,37.84561181640625,0.005159610882401466,0.438027914541739,1,0.6410773539212297,True,0.7076677381179666,0.422748689755101 +14309000,3,40.7951953125,13.5983984375,202.1301,17,WestMnts,Non-ref,OR,42.824843,-123.178949,4.048369407653809,19.561841162478164,0.8236212762823882,938.3286635147465,1267.5255359521457,0.781572313605919,17.318528694654606,2.128036949830394,47.76835314031866,1011.1948565159607,0.17589381002785875,0.1347657045306956,0.03599721991481151,0.556753233438857,0.5541104623614407,8.640306829263574,-13.357771936907008,0.0022979976455640063,0.0,0.0,2.392364204042243,37.84561181640625,0.005159610882401466,0.438027914541739,1,0.5990075366723981,True,-0.9975025757364964,0.0974461086619755 +14309500,3,34.9736005859375,11.657866861979166,224.9208,17,WestMnts,Ref,OR,42.804004,-123.610907,9.073582649230955,20.129310084765148,0.7709252940423699,665.548278621959,1284.1919620862734,0.7610832353257997,16.962190307400252,2.0903152529978315,46.08483280633445,971.0879919852415,0.06180607997518318,0.13522576598527886,0.03092848252912704,0.6055901901940771,0.538822279835328,9.014010858572684,-12.70726746017007,0.0,0.0,0.0,2.3568925248710424,31.786068359375,0.0072311339899897575,0.36789430971498843,0,0.0,False,0.7712974850721274,0.7923413447206061 +14310000,19,204.29697854614255,10.752472555060136,1194.809,17,WestMnts,Non-ref,OR,42.92345,-123.428957,29.49486351013184,20.48790526596024,0.9220958783278422,689.1475615360047,1173.3979225553906,0.7574158636728541,17.209413414814524,2.342439964546878,45.60347970278415,1044.915905621213,0.11078958285265206,0.13956119866701747,0.03015854813374782,0.595635220477214,0.547878149788649,9.336358984326283,-12.828894321642546,0.0004979751131472242,0.0,0.0,3.1115743008405663,128.76770681762693,0.0025068316608667374,1.4903669770558672,1,0.0777145152918871,False,0.7972043931678076,0.7076519727415477 +14312000,85,697.0157565002442,8.200185370591107,4234.129,17,WestMnts,Non-ref,OR,43.133172,-123.398405,99.64636993408205,20.499228754006868,0.9459339633373123,683.6222865907088,1176.2779405749836,0.7258608052958069,15.867355612046437,2.2281524847344234,45.09613902792365,1066.7437746165765,0.11281669417726761,0.1431192276827183,0.03528946667600478,0.5654174829602207,0.557849502781356,9.56376350495238,-12.730535796886947,0.0007029957106355231,0.0,0.0,3.6297058190532776,182.51402163696287,0.0006076927529647946,2.1124308059833665,2,0.0281940264943907,False,0.8983174686269579,0.9040986114354737 +14316700,19,85.11111709594726,4.4795324787340665,587.8989,17,WestMnts,Ref,OR,43.349842,-122.728942,27.353796005249023,15.94633609699464,0.6516886271571414,966.2875864545618,1578.0255233146515,0.7601439820154624,19.113286511265656,1.8671991844675506,49.40677484411464,993.3445202888621,0.12542289014406638,0.17214142065404564,0.05832333648167967,0.5078107300670971,0.565796170486953,7.808094810279581,-12.470000267028809,0.0,0.0,0.0,2.7694580698460753,35.83465393066406,0.01355801708996296,0.4147529390123155,0,0.0,False,0.8826049874840111,0.9329542156396854 +14325000,11,96.17150115966795,8.742863741787998,443.0745,17,WestMnts,Ref,OR,42.891499,-124.070652,27.469209671020508,19.94100599864326,0.5100341142947084,564.0614877303456,2020.5966898192496,0.7808432232951422,15.245719162628443,2.130891408468875,46.42374216194084,979.3579667783334,0.14202409446179134,0.12572890277968884,0.018807010194645445,0.6446718342958987,0.5138974373283763,9.612305780036264,-13.396026867823323,0.0,0.0,0.0,2.751496949948118,74.78674792480469,0.002051376271992922,0.865587360240795,0,0.0,False,0.729998135490185,0.6418288575606694 +14330000,27,221.1112429428101,8.189305294178151,985.7133,17,WestMnts,Non-ref,OR,42.730405,-122.516425,47.93740463256836,14.922529724927994,0.6688215532957382,1447.6024428731998,1452.4271377248422,0.67230061227902,10.269772644478877,1.9221931771922998,50.95223268347076,958.1655877590935,0.11523939370624396,0.25358219967869194,0.1898924361712818,0.550229906339861,0.5787864061133178,5.854450953741612,-12.519258901939855,0.01992744605778997,0.0,0.0,2.9906527011011277,72.06908895111086,0.02297550067305565,0.8341329739711905,0,0.0005441021935597,False,0.679130736925593,0.7803925859428402 +14332000,5,42.18090893554688,8.436181787109374,217.3068,17,WestMnts,Non-ref,OR,42.708184,-122.392809,3.8565495014190674,16.167022817125332,0.6853739135717622,1561.069297566507,1404.0902476798822,0.6546873192317895,10.16655625939634,1.9554130668118128,50.42341908549441,926.273371523298,0.18803931646792363,0.2077475446849314,0.17412109816914315,0.560835118639889,0.5711940701380839,5.435508583608863,-12.470000267028809,0.0024406508268338682,0.0,0.0,2.398179230050001,34.1464130859375,0.037013132125139236,0.39521311442057294,0,8.561539295991638e-05,True,0.7341261322017303,0.8011514312512902 +14337500,15,98.6279542541504,6.575196950276693,640.8351,17,WestMnts,Non-ref,OR,42.651237,-122.691429,9.07650661468506,22.11116211662985,0.9831880073408575,1104.1835946250899,1096.943826876631,0.7204846662428414,8.532218366928259,2.0325106762553395,42.25633901941656,1034.1137683200932,0.14913566443188053,0.13669011551273416,0.07059224870542441,0.51576922781295,0.5627164517823282,8.186344777805218,-12.686849031143247,0.00660290286516349,0.0,0.0,2.80529218388009,47.61200198364258,0.007098150439560413,0.5510648377736409,0,0.0501593610779179,False,0.8996039223547686,0.933249389589492 +14339000,81,578.8042112960816,7.145731003655328,3154.809,17,WestMnts,Non-ref,OR,42.524847,-122.84282,92.17188262939452,18.734662910104934,0.8701546491307828,1197.7516448148838,1261.4984496828458,0.680140691188233,10.75268296238968,2.0772856535698154,46.42221508344573,1013.0113070724653,0.14039669668270405,0.1906868243236199,0.11984245378922202,0.554639533438233,0.5701587408743138,7.4874158076108035,-12.993407397504209,0.009292688591678921,0.0,0.0,3.5063497028424733,130.86304940032963,0.0025182338431477547,1.51461862731863,1,0.2671524792214699,False,0.6876231176574025,0.8411926194403152 +14357500,19,114.7519735107422,6.039577553196957,721.8189,17,WestMnts,Non-ref,OR,42.324015,-122.866706,6.289606094360352,22.857650784268564,1.6491135979778349,973.2101356754665,688.7562764733655,0.6020629959570114,10.792924937991891,2.0992245273895485,44.81285255825464,1087.124216735649,0.11405221302438746,0.08650648501701752,0.05215618524117187,0.5117646316932629,0.5605137899995362,9.186648185419214,-13.779919389206853,0.0024589680254622796,0.0,0.0,2.88945759892821,59.108456298828116,0.004333344288170338,0.6841256516068069,1,0.4009074668692425,False,0.7706206486394351,0.8701345251434576 +14359000,135,943.9219323043824,6.992014313365796,5310.432,17,WestMnts,Non-ref,OR,42.437346,-122.987268,112.870361328125,21.09007425495232,1.1533716069421374,1092.8344714988293,1059.2280294823436,0.6509603323475167,10.240245734697663,2.0825981744220674,44.41909846812501,1048.900321875779,0.1333451509330187,0.151471382728673,0.09784894429154582,0.5402314480755944,0.5665644690397518,8.217174452511749,-13.248774376371083,0.006712385144378278,0.0,0.0,3.727083406491237,149.49542073822028,0.0010677651735022664,1.730271073359031,2,0.2471114934929634,False,0.8357585689340474,0.9073706035558364 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+14210000,37,288.2040067138672,7.789297478753168,1763.227,17,WestMnts,Non-ref,OR,45.299843,-122.353975,97.85054779052734,14.362297077557757,0.46839353266990014,1028.340875182432,1876.637868821275,0.7183293646318466,13.97468876408668,2.1179079801098815,47.258847964882335,852.6977313155602,0.09516044781268652,0.2617363919643617,0.14503868589616886,0.5182610545657612,0.5969327233647903,6.636505800823744,-12.470000267028805,8.801854322885251e-05,0.0,0.0,3.250277202387942,109.96126538085937,0.005462613422423601,1.2726998307969835,2,0.0446720828117343,True,0.8994356062936408,0.9417537511798592 +14211550,3,31.46334301757813,10.487781005859375,137.3274,17,WestMnts,Non-ref,OR,45.452897,-122.64315,3.0018763542175293,20.128561126850556,0.7537936014176566,173.8206824828941,1307.2510770911322,0.6126341664097245,4.32736531446599,1.8870133157932145,43.07072955180102,970.4279702264954,0.10968344419057267,0.06302168457519508,0.030402255308808305,0.4795987468840855,0.5725000947153702,11.26795629315921,-12.470000267028809,0.015473305248663074,0.0,0.0,2.1633255376524025,29.9852294921875,0.004733227659016848,0.34705126727068863,0,0.0,False,0.6073002968016858,0.5231066725783935 +14211720,601,5664.872174591064,9.425744050900274,28936.87,17,WestMnts,Non-ref,OR,45.518452,-122.667874,1271.949951171875,19.819459271963016,0.6306396620946265,578.1772611208781,1662.9591130860938,0.6813854933573426,10.469292323445748,2.232886941832015,41.28142264943803,945.575173447976,0.09623859849957746,0.15899782452198596,0.06466200466108031,0.5449941763792843,0.5768323499552509,8.999253033751001,-12.499990539286934,0.004612311547809924,0.00010960743324330322,0.0,4.462940341363427,471.80318359375,4.9882768507814035e-05,5.460684995298032,17,0.0822651567777201,False,0.7629341006322333,0.8653166392019667 +14216500,5,57.5007119140625,11.5001423828125,349.524,17,WestMnts,Ref,WA,46.075669,-121.998698,32.482418060302734,11.489728254109346,0.28132950019991326,872.2185409709706,2705.151499888572,0.6646903733591986,16.23645152181226,2.0606305962172633,41.38895692976209,751.8993566045664,0.1275509629541645,0.3324990298545945,0.20345056259966665,0.5697529055791205,0.5987974364828172,6.61074676670939,-12.47000026702881,0.010372878491068237,0.0022984622871033912,0.0,2.5411964255920907,30.055556640625,0.007371267303824425,0.3478652388961227,0,0.0,False,0.8002240842866039,0.8847748822130976 +14220500,37,317.42223294067384,8.57897926866686,1898.358,17,WestMnts,Non-ref,WA,45.951779,-122.56399,170.02500915527344,16.72957915128617,0.2981825789773067,819.4577074297326,2792.3274260386816,0.6984831193708876,15.01436003375214,2.1882919412801236,38.55800882469872,803.3129683205393,0.07002665189824862,0.27837770799926376,0.16176778460614563,0.5597424436003154,0.6019696305154213,7.207740812971331,-12.470992115888176,0.028150906095662107,0.002495589297073123,0.0,3.2809824326672996,115.77003332519531,0.0025989972054958344,1.3399309412638347,3,0.4665661024888176,True,0.6860681176619114,0.8337862782538975 +14222500,9,53.68785607910157,5.965317342122396,323.8893,17,WestMnts,Ref,WA,45.836781,-122.466208,25.14309501647949,23.731236887570553,0.3155385674466174,633.2380384122521,2859.7782272757813,0.7163384496963642,11.52498281686268,1.8978806630843763,31.4330267122192,860.3701411860923,0.1342527008889402,0.2085246681410431,0.07754148669879142,0.5086070490458692,0.5960056046774753,8.368936366404023,-12.470467646710103,0.0,0.0,0.0,2.543443951595077,36.59741186523437,0.007338334806263447,0.42358115584762,0,0.0,False,0.8785026435226037,0.8999274769085974 +14091500,17,151.63532946777343,8.919725262810202,818.0802,17,WestMnts,Non-ref,OR,44.626227,-121.483943,50.99807357788086,14.457875206526838,1.2770859565665835,1236.4544740302736,997.3879788892149,0.558725908963553,10.491905455116688,2.1168981695395757,51.030447426997966,938.3140481519279,0.0460121965708415,0.3614478276749008,0.27537723780099077,0.5290363745514943,0.6049294302648316,5.910003683561647,-12.470000267028809,0.0017973921370349837,0.005302133396161044,0.0,2.9222403520480364,63.72827868652344,0.006563712377101183,0.7375958181310583,1,0.0154524032326468,False,-0.2160466661628546,0.2981893589106776 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+14145500,25,138.97004718017578,5.558801887207031,1016.985,17,WestMnts,Non-ref,OR,43.722068,-122.438659,41.38704681396485,13.398861663150672,0.5666421331431865,1227.0675763159784,1693.9148968818172,0.6995343315141035,16.176002983707058,2.063066238584513,51.877850708204164,940.5456026904205,0.09030528393413972,0.228200782025878,0.11988519953415619,0.5098444718526778,0.5764420110846923,6.458024340402548,-12.47000026702881,0.010478842905298559,0.0,0.0,3.008543259104918,61.31503833007812,0.02914099022746086,0.7096647954870152,1,0.4200455756615563,True,0.4241704561982182,0.5632970751327513 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+14158850,15,89.30371588134766,5.953581058756511,480.1356,17,WestMnts,Non-ref,OR,44.2679,-122.049783,34.53677749633789,13.289041580974242,0.3806076708760303,1271.366187136814,2262.8039132337462,0.6401194677622544,9.86493154932554,1.9035766632339168,49.182232116483156,852.5093598430401,0.06765081217231447,0.33465527913768395,0.19770311177502287,0.5360112792465976,0.5964852150389671,5.320913845310047,-12.470000267028809,0.0025943562837614656,0.0,0.0,2.7202582643075015,43.98721405029297,0.009916361421346664,0.5091112737302427,1,0.0235735606716409,True,0.5750827895585024,0.6469468461517802 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+10348200,65,420.1903767852783,6.464467335158128,2762.546,16,WestMnts,Non-ref,NV,39.517617,-119.741644,31.387252807617188,16.506294810076827,1.4108420485181086,2065.535597298186,830.7934858483945,0.5033080136048897,10.103652869847767,2.2067250051469633,54.695940781195006,997.1673121710467,0.11872805458927627,0.23423767732733233,0.28340801762064743,0.6743307028171742,0.5352345539697462,5.680259384678886,-13.229694222078301,0.1827520140098699,0.0,0.0,3.4465661736971884,161.08420983886717,0.0011414875043556094,1.8644005768387402,6,2.2633263658901988,False,0.7417033801276123,0.7561403899070079 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+10350000,93,569.8273266830445,6.127175555731661,3697.635,16,WestMnts,Non-ref,NV,39.520467,-119.70102,36.31075286865234,17.78637254127331,1.9445765087512308,1969.112699631456,716.8727549554475,0.4581056393782741,9.769267680429712,2.134728461449301,52.637605697893065,1035.1786066338193,0.12414483324960882,0.18849064587076603,0.25477120011348486,0.6538436396050884,0.5365264328012417,6.368745813708603,-13.38574188933316,0.13895404265535208,0.0,0.0,3.5704521833450373,168.1064393005371,0.0014969907933846116,1.9456763807932533,6,1.9053776284878483,False,0.7648088985323475,0.8058168073971138 +11314500,5,64.7417216796875,12.9483443359375,440.9982,18,WestMnts,Non-ref,CA,38.493523,-120.221027,8.451157569885254,15.006851067614296,0.6502348463348206,2190.919771519734,1373.4877454163259,0.4562400781768864,14.605768745212808,2.235069925334257,57.55415967546661,885.99424994422,0.15124509367076427,0.3593951706936489,0.390541263064657,0.6588397064102388,0.5022422418386536,4.734250510808825,-14.070007326899994,0.0030089695748631534,0.0,0.0006246535090045682,2.6488868843820037,52.922427734375,0.0220530703663826,0.6125280987774884,1,0.9124670108240902,True,0.6275924950728576,0.6778025432564336 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+13055340,65,567.8798012084961,8.736612326284556,2764.828,17,WestXeric,Non-ref,ID,43.835,-111.777778,11.841602325439451,20.926944686046845,1.45205311465578,2035.0346055251505,786.062307733905,0.3850559199661365,7.39552254871792,2.1221680232600404,37.213045985466245,903.2700377513506,0.10638320982534728,0.4153937007217004,0.36765452807433086,0.2496809823069357,0.6555658469825972,3.3876378897054735,-13.190131799671638,3.2011353986163355e-05,3.721831545919317e-05,0.020828369414703205,3.4494301066918553,148.48895651245118,0.0010659161489456892,1.7186221818570737,0,0.0,False,0.6181956701378319,0.5726580222793004 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+07176000,377,3269.659840538025,8.67283777331041,16709.18,11,CntlPlains,Non-ref,OK,36.307319,-95.698037,139.5622100830078,28.974779845598377,1.1689430029521692,293.4338463294288,1040.6615333340762,0.5040591487415017,1.958271861306074,2.221300774380543,26.56713449215562,1214.294527319526,0.13115636381324838,0.0075213836372846855,0.023985373563186804,0.31975852456493564,0.4841769930577801,13.987657743126013,-15.050000190734862,0.008960241484074052,0.0,0.0,4.224478213155065,465.6012047729492,9.592462447471917e-05,5.388902833020245,7,0.1897522895418139,False,0.0271864725559772,0.5551185685700073 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+07195500,37,298.79514862060546,8.07554455731366,1629.999,11,EastHghlnds,Non-ref,OK,36.130082,-94.572164,19.17030334472656,27.398335200271863,1.0008800657031882,376.9296459952797,1213.9749211586209,0.6005831979584711,2.8729315964666022,2.1553061113988012,38.18239224153227,1214.6047984436927,0.17186056802588245,0.00480456129493501,0.020357452281568655,0.1833856880823907,0.4624503281576049,14.12657538109752,-15.05000019073486,0.004187515448067927,0.0,0.0,3.245787448000267,106.7612900390625,0.0006924184272065759,1.235663079155816,1,0.0587220887037313,True,0.8651639191917471,0.8756622245363498 +07195855,3,31.1295703125,10.3765234375,147.1878,11,EastHghlnds,Non-ref,OK,36.216193,-94.604387,1.3049240112304688,28.15542760524106,1.0119586821518747,373.60173344768816,1209.8967643606672,0.6181750461867824,2.1907969959162616,1.8621425828209555,37.47659366413337,1224.3355736886183,0.14300860484900052,0.006609515325152567,0.020580929250952677,0.18607330213125156,0.4660048074025636,14.188416211240444,-15.050000190734862,0.004895514183480576,0.0,0.0,2.1642395891684534,25.88411083984375,0.0026656396221369505,0.29958461620189525,1,0.557590443539472,False,0.5450404268281592,0.703550377739411 +07196500,47,439.04733026123046,9.341432558749585,2454.301,11,EastHghlnds,Non-ref,OK,35.922869,-94.923566,28.08160400390625,27.576562917054805,1.007349684728043,363.84513665959906,1214.27968934939,0.6059053563873901,3.0358758054412496,2.2766476276112093,38.014437729270234,1222.8958181983935,0.16229416884340905,0.00438524618364592,0.019555038254319312,0.1848868431257666,0.4620785493738445,14.313106485496135,-15.050000190734865,0.0035844714637628316,0.0,0.0,3.3939969242148798,179.390482421875,0.0008316035382449627,2.0762787317346643,2,0.0642880890687405,False,0.6490374228930593,0.7522903409864071 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+07335700,3,33.964683898925784,11.321561299641928,102.5696,11,EastHghlnds,Ref,OK,34.638437,-94.612724,2.43060040473938,24.658550579642917,0.8324823387462423,291.29938510929685,1497.4809705173382,0.688389577899351,9.61329330281814,2.132896003908189,39.65593505533197,1242.9704863955444,0.18442745433481436,0.015984577604105512,0.012747481474460084,0.1621544169430976,0.4329495114727847,15.287479286333996,-15.050000190734865,0.0,0.0,0.0,2.3629607754816275,33.33226098632812,0.0034632401075214148,0.3857900577121311,0,0.0,False,-0.7362406996953941,-0.8270475535076289 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+04036000,7,73.5853271484375,10.512189592633929,419.0814,04,MxWdShld,Non-ref,MI,46.587447,-89.54182,4.549838066101074,13.021724855066491,0.9370837832709975,431.1903916445493,891.8684605671323,0.5323746784149125,2.1223326979918085,2.131086761771725,51.86097979761101,833.3111222073283,0.020526920576078144,0.43463174099824403,0.2479877336120786,0.2396818675575368,0.7024698709626475,3.793677284661289,-14.099639311503793,0.09334062463313195,0.0,0.0,2.6501233779390394,53.875623046875,0.002864773152396083,0.6235604519314236,0,0.0,False,0.3587163965170376,0.5272638589429746 +04040000,75,706.8526971435547,9.424702628580729,3494.206,04,MxWdShld,Non-ref,MI,46.720774,-89.207086,37.255950927734375,13.70299148770711,1.0373327605526121,427.42998354987054,813.2089800514099,0.550182085425968,2.4649214995668407,2.14978723395417,51.777425394032605,840.1698306813872,0.04066532023772421,0.39852860426076,0.2304317100689907,0.29031685377680516,0.7006956227926338,4.059039774946359,-14.248309998393067,0.0305877606731062,0.0,0.0,3.5531075522778703,148.29932446289064,0.0006898679421283305,1.7164273664686416,2,0.0548674137994481,False,0.7016709020012725,0.6534306680724575 +04040500,9,83.61006176757813,9.290006863064235,419.8266,04,MxWdShld,Ref,MI,46.584106,-88.57597,5.493801116943359,10.969330296875837,0.9666979661571903,494.5748849392761,867.233757117824,0.5537665336643929,2.2799905090585297,2.0803041185166444,57.34816815161225,836.3414622616024,0.05223246696251641,0.4531627598233421,0.23800470868874077,0.2754001797124437,0.7005604144358349,3.8006506362505847,-14.316133740270773,0.018139865841346046,0.0,0.0,2.7019805458407715,46.9446953125,0.004240379668772221,0.5433413809317129,0,0.0,False,0.687083664069753,0.507476900038354 +04041500,19,179.3149647216797,9.437629722193668,869.9346,04,MxWdShld,Non-ref,MI,46.726323,-88.662079,11.21029567718506,11.772333421646938,1.0048382757431633,461.8062301068327,833.893402601564,0.5572885410289965,2.3263927931905504,2.06893687979368,55.523942607017986,835.1100586598486,0.0664276726673827,0.4255732176102418,0.2269493353282014,0.2984009333849008,0.6998897176923301,4.008429912572942,-14.315280768127202,0.01748987481121062,0.0,0.0,2.9536881430298156,81.66146899414063,0.003381936112418771,0.945155891135887,0,0.0245700010084808,True,0.7504648523470043,0.7189676508950067 +04074950,25,264.31877310180664,10.572750924072263,1205.531,04,MxWdShld,Ref,WI,45.18997,-88.73344,11.86288833618164,11.565301282916908,1.0691285566489992,482.49462315608656,788.0019028750078,0.5544186947715518,2.3904804097442742,2.247870564311797,56.647364875005735,842.2316860942917,0.08074344216959031,0.32227086308910163,0.18238007307934315,0.36852688324807154,0.6879311397989712,4.383861665422885,-14.05000064416452,0.051363304854312994,0.0,0.0,3.116706083917137,140.0177032775879,0.0027375705540180206,1.6205752694165267,0,0.0213026381956406,False,0.8083540256868349,0.8386102506101186 +05391000,49,437.14980420684816,8.921424575649963,1947.559,07,MxWdShld,Non-ref,WI,45.830509,-89.552364,18.27361869812012,8.040783796357857,1.034591128964489,508.8639906202242,798.8680921169624,0.581584694365559,2.1541157165466394,2.135180519761598,65.09648964310489,826.3658619809568,0.1616330304554995,0.47491555710741745,0.1962184216906955,0.3716284903832212,0.6970253714935438,4.000372354835295,-14.050000190734862,0.10926008249352522,0.0,0.0,3.3296532446563187,131.03336206054686,0.0002689113316591829,1.5165898386637369,10,1.2826253923796298,True,0.1343230316071105,0.546301299880196 +05393500,5,40.4571826171875,8.0914365234375,220.4352,07,MxWdShld,Ref,WI,45.449132,-89.979311,2.625295400619507,18.318451384229174,1.0365119603633177,496.94964237026323,804.2798247979632,0.5565413594633617,2.271425240561021,1.9335118942843241,43.62387220186559,833.6503658443742,0.2378880602938929,0.4313500368936037,0.17099482705385335,0.4008445558179475,0.6910649613143067,4.172281303534977,-14.050000190734863,0.0096226368835871,0.0,0.0,2.3446448632856414,25.8040966796875,0.0019805163610726595,0.298658526385272,0,0.0,False,0.8527002302476759,0.9173464015850784 +05394500,5,97.62095947265624,19.52419189453125,483.1974,07,MxWdShld,Non-ref,WI,45.235799,-89.649848,4.821055889129639,14.52775117021591,1.0497300714119897,478.5188735279661,802.9850762439994,0.562160056786108,2.144537059786885,2.2168778095018276,49.472922005679486,842.9292744951588,0.1959255499160467,0.35130994386672615,0.17768232753225327,0.38612562130042816,0.6894830440738742,4.588800858535653,-14.050000190734862,0.010482181072492087,0.0,0.0,2.6767032754597806,57.81556591796875,0.002188305603340268,0.6691616425690827,0,0.0,False,0.8750154588105759,0.8120287236900672 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+05398000,245,2076.890601539612,8.477104496080049,10421.35,07,MxWdShld,Non-ref,WI,44.885802,-89.634844,99.2753448486328,12.733820141271025,1.0523724743170024,471.462886435905,799.7820345685408,0.555621217462418,1.9242112690470552,2.138253964090696,53.140383736284235,841.5392726726851,0.1607484679593653,0.39460300178309893,0.17839723506127564,0.38725709741767284,0.6897633557740047,4.484724756993145,-14.05000019073486,0.050533177552328126,0.0,0.0,4.019558730292144,287.7913563232422,0.0007281124126166105,3.3309184759634514,19,0.4249328326439671,True,0.873014489355868,0.917017857507733 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+04170500,11,83.491443359375,7.590131214488636,407.5569,04,CntlPlains,Non-ref,MI,42.512533,-83.676329,3.3735244274139404,22.949515028064727,1.1015725854209422,298.02513319115377,836.4020144167667,0.5309420721433106,2.109030341664544,1.923538033459095,34.560234003227976,921.1824120380808,0.15826886862810183,0.19490896339767558,0.1541543875291372,0.18448581921594912,0.617874838562977,8.604072626205207,-15.050000190734863,0.06058201657469962,0.0,0.0,2.5987647676532837,56.638870361328124,0.0005177136627025902,0.6555424810338903,1,0.2576057045412966,True,0.6429226644712613,0.6909145453698142 +04172000,23,173.72072888183592,7.553075168775475,829.2179,04,CntlPlains,Non-ref,MI,42.465312,-83.799943,6.622974395751953,23.047481621296832,1.117178556552588,294.3330956935043,835.1638292959716,0.5263069960423339,2.0970404391510145,1.9481522925582189,34.51673935688889,932.8726519537977,0.15429231142459715,0.19427555214580408,0.15468665165407675,0.18048994315506298,0.6174594161638433,8.58158472537455,-15.050000190734863,0.05250132840336428,0.0,0.0,2.923021827999017,78.47949926757813,3.0380582757061347e-05,0.9083275378191914,1,0.164698860192935,False,0.7380219479185421,0.8504410366865571 +04175600,9,84.84332049560547,9.42703561062283,330.7972,04,CntlPlains,Non-ref,MI,42.168095,-84.076058,2.864980697631836,22.999693189570518,1.1198175297070896,298.3365487262406,879.4919175337493,0.5067019691051277,1.694537984042666,2.0705437355743816,34.40213813190856,984.747694702667,0.062319880349516306,0.2004720085126144,0.14394986197690757,0.1948082336068502,0.6116179734046511,8.546510929584882,-15.050000190734862,0.04208906501261877,0.0,0.0,2.6298818268730884,59.364923828125,0.0011794043239206076,0.6870940257884838,1,0.7500309942432013,True,0.2041235646410607,0.3942825763742519 +04176000,27,319.63249127197264,11.838240417480469,1195.18,04,CntlPlains,Non-ref,MI,41.904214,-83.980499,11.092364311218262,24.050638114858774,1.114819417309901,279.69919162348634,894.0556133959658,0.5001340074211029,1.5869021179105995,2.0742321236903005,33.112505446535486,996.5413318948376,0.06750189009237632,0.2177087216531429,0.14313100045044247,0.18668182716325124,0.6098940302953284,8.699085716741484,-15.050000190734863,0.027353933692208173,0.0,0.0,3.1058887969239795,106.65690624999999,0.00022279436234384775,1.234454933449074,1,0.1984700605722363,False,0.7596529300173113,0.8149498892028585 +04176500,55,673.5616008911132,12.246574561656605,2685.966,04,CntlPlains,Non-ref,MI,41.960601,-83.531046,22.9185848236084,24.614377981523546,1.1189683616917114,250.4447526215733,892.6843472242043,0.47218573029302985,1.3024149812629822,2.1521566054728445,32.58268992646597,998.6191488926445,0.06857641943180429,0.22294293998767017,0.1427306996373744,0.17616398906997138,0.6087778850583302,8.913113046221374,-15.049999239178518,0.014758019886555413,0.0,0.0,3.43651313094252,203.21699999999998,0.0005601785960607231,2.352048611111111,1,0.0973766174125434,True,0.8254419031378375,0.9112934241867204 +03261950,3,69.9942392578125,23.3314130859375,394.6005,05,CntlPlains,Non-ref,OH,40.306992,-84.383838,3.84621787071228,29.260040551844956,1.0298217570491852,296.64471057854126,993.2960165137348,0.46128352327968086,1.029694523875952,2.3263928183568248,23.329328005354185,1022.7934305886951,0.18509612522366464,0.13803920365019265,0.11103224145082181,0.1922805267781619,0.5760204764378999,10.021195338663407,-15.050000190734863,0.012432531452984896,0.0,0.0,2.6330791392021924,48.9024130859375,0.00038510520244017243,0.56600015145761,0,0.0110043830139314,False,0.7753281466369709,0.8134089484730531 +03322900,27,237.8217902984619,8.80821445549859,1293.521,05,CntlPlains,Non-ref,IN,40.656157,-85.032747,11.619976997375488,29.45736886998699,1.0533576844883747,276.8947326831726,974.4313072424479,0.44371097721479164,1.0019107981163176,2.1824568294003397,22.643154048276642,1026.3423950704064,0.15803123014350512,0.16288224349683442,0.11527030448556803,0.20644062946916641,0.5787868674324375,10.021709231741289,-15.050000190734862,0.027876314407087193,0.0,0.0,3.1200164780580786,109.496634765625,0.00017404869140591472,1.2673221616391783,1,0.1771626995239514,False,0.5698555726752357,0.7606255385095999 +03325500,5,41.729515625,8.345903125,345.3102,05,CntlPlains,Non-ref,IN,40.280044,-84.992468,3.5225367546081543,29.686224932500526,1.039841141139754,316.9577231067122,984.9429946261201,0.4253660939272038,1.0609879629833034,2.1128159831827813,21.988359303529272,1024.168759695918,0.1474548708977404,0.14673531682375768,0.11495410449460682,0.20184787418409186,0.572584691545276,9.831024207320546,-15.050000190734862,0.0,0.0,0.0,2.540317722918768,30.050564453125,0.0006720192031934857,0.347807458948206,0,0.0,False,0.8165740335457558,0.8180789050856423 +04178000,37,358.5517617797852,9.69058815621041,1600.184,04,CntlPlains,Non-ref,OH,41.385328,-84.801626,16.880342483520508,25.21131077714867,1.0543971719053595,290.1754559690374,939.8696298874484,0.47288744696813767,1.595501194203925,2.1155960874999367,30.765930826729555,990.7662093576764,0.061382357689142256,0.20993284150720376,0.14043497427376134,0.1899090064460976,0.6030613631602775,8.721099432877583,-15.050000190734858,0.012317275402810059,0.0,0.0,3.212754599841816,121.7772406616211,5.270183100947179e-05,1.4094588039539477,2,0.0766981832405111,False,0.486263355748107,0.664778759367043 +04180500,57,573.4305257568359,10.06018466240063,2716.033,04,CntlPlains,Non-ref,IN,41.177271,-85.055803,30.57564353942871,24.931697160175347,1.056267124172807,281.0533486630937,948.8566131028606,0.47669197007678266,1.5517732439684808,2.1971761255825712,31.803118128553393,1001.9780953850042,0.0806282075060032,0.21321593953062903,0.1367620735489782,0.19347532877362042,0.6008013273209069,8.932785864284977,-15.050000190734863,0.009152754902267126,0.0,0.0,3.426572381514669,184.44237493896486,0.000276390986982733,2.134749709941723,2,0.0449991375056528,True,0.5510493366900096,0.661077381738669 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+04165500,49,445.0115450286865,9.08186826589156,1892.565,04,CntlPlains,Non-ref,MI,42.595867,-82.90881,18.910350799560547,21.99070942417723,1.1185101668475446,249.8936122747787,841.6368079143556,0.4795663950209633,1.5239627237231792,1.976046347299325,38.27773659459932,941.2633310989171,0.14944278762907565,0.19583791649999982,0.14710602220447766,0.18883299443623086,0.6187976706415195,8.903233924168841,-15.050019279341747,0.02367837643515327,0.0,0.0,3.2900709929986367,104.63850898742677,0.003504939842969179,1.2110938540211431,0,0.0294446744100302,False,0.7650927324289323,0.8133642330107279 +04166100,7,45.05985314941406,6.437121878487723,224.3466,04,CntlPlains,Non-ref,MI,42.447813,-83.297709,2.385982036590576,21.85245773748165,1.1375010023827912,247.4963418764013,825.5755702021153,0.5149593020972676,1.675957932702514,1.7879113920712462,38.41807252303519,939.0412625018292,0.10067275014487818,0.18097815142259296,0.14184568527487018,0.17180416211844132,0.6167718284338306,9.087291156295134,-15.050000190734862,0.03578240327387291,0.0,0.0,2.3578402228048363,27.257290527343752,0.0007186614093370736,0.3154778996220342,0,0.0064155947902438,False,0.7237168937673502,0.8362377972674977 +04166500,13,97.60077905273438,7.5077522348257215,475.9533,04,CntlPlains,Non-ref,MI,42.372259,-83.255485,4.728065967559815,22.062819600832725,1.1323107092105142,234.16973058261348,833.637369680094,0.499803553897238,1.523134328923069,1.9191661939440383,38.64216616667353,943.876135765142,0.09585046152422708,0.1529652904701324,0.13911021521890016,0.16512610929339455,0.6152916686317877,9.172754180519286,-15.050000190734863,0.02371326402694525,0.0,0.0,2.6863545988203303,45.24407763671876,0.0005513643845915794,0.5236583059805412,0,0.0031884617810352,False,0.6778370347577216,0.7236921984770898 +04167000,5,68.71296875,13.74259375,229.4964,04,CntlPlains,Non-ref,MI,42.348093,-83.311597,2.834556818008423,23.145111313187073,1.1235441017192427,244.3404033889073,848.1542349026715,0.46697164649565553,1.4824612491759435,1.9838414908543782,36.96730565376265,952.9658144056641,0.12339274790549368,0.1623228824419418,0.1439532058814934,0.15762906503888624,0.6138605316130821,9.087717367419554,-15.050000190734865,0.014173840187188246,0.0,0.0,2.452694904979673,49.6218740234375,0.0006895418628118932,0.5743272456416377,0,0.0209838798504434,False,0.7401975085616281,0.7853511446096377 +04168000,3,41.6420576171875,13.880685872395832,219.177,04,CntlPlains,Non-ref,MI,42.300593,-83.300208,2.67903995513916,23.64726439384733,1.1425441938888135,218.36088413461283,849.4294000117766,0.4454825723344219,1.3337173726650646,2.063857945908552,36.58594494828985,970.4411184254146,0.037295685163177744,0.1855141451520737,0.14207444504912636,0.15078318583358707,0.611736603031918,9.256415895091688,-15.050000190734865,0.003746238747629074,0.0,0.0,2.3539653131240423,34.9405107421875,0.0008041503024287522,0.404404059516059,0,0.0,False,0.6380652186063986,0.5352817894579152 +04195500,27,294.5836633911133,10.910506051522717,1080.029,04,CntlPlains,Non-ref,OH,41.449496,-83.361316,10.92651653289795,27.429484536011767,1.1216141942170552,218.3583778314204,886.5796686189859,0.41456023697520833,0.7422993215419131,2.0482310818689076,28.434768152678817,993.940173666024,0.1222264643684313,0.19242022513610774,0.12287507239855534,0.1824433708906895,0.5935510100552409,9.71033714006044,-15.049762616423147,0.0016955630246831038,0.0,0.0,3.069512307647876,87.9351328125,0.0005885513382963836,1.0177677408854167,0,0.0006054307397626,False,0.8520116568258036,0.8614654736219097 +04200500,15,183.7789183349609,12.251927888997397,1026.93,04,NorthEast,Non-ref,OH,41.380324,-82.104593,10.000940322875977,21.79849593240798,1.0159585537669533,278.26971332248473,964.9810814656823,0.530528114110411,1.385802715880301,2.206071101477157,34.93676252268547,980.2076311864562,0.16820254888899935,0.13511585923015268,0.1216752407235842,0.15479939891772115,0.5833815745630415,9.509975587625169,-15.049106641100025,0.002633991256374102,0.0,0.0,2.9744618562921086,90.0315498046875,0.0029710482340306044,1.0420318264431423,0,0.0005924999290782,False,0.7292587248306958,0.7501354188878773 +03136500,13,107.65850317382812,8.281423321063702,520.659,05,NorthEast,Non-ref,OH,40.405618,-82.499885,6.491342544555664,23.516051458440796,0.9718179157933932,363.1013021228229,1034.909805389432,0.5218049839703622,2.02407347883154,1.9579000146880072,31.213608316534902,1005.7705831664457,0.21302319857816845,0.12744334760071716,0.12133424312619873,0.1623503318961476,0.5801068410986391,9.1488938084186,-15.050000190734863,0.004305015264328262,0.0,0.0,2.74304479324411,44.514489746093744,0.0013638344826176763,0.5152140016908998,0,0.0258776612262274,False,0.6389082089928464,0.6597724692050054 +03139000,29,205.7019643554688,7.093171184671337,1202.849,05,NorthEast,Non-ref,OH,40.481454,-81.985979,12.877519607543944,21.45008683365984,0.9990483917421192,334.961757381514,991.19518121709,0.5599958906907399,3.2807257073613014,2.121064565075713,37.49069088735618,990.1420182578661,0.1903521874856879,0.10808070993393207,0.1162605041536171,0.1817789814355097,0.5783715380088589,9.382791239022009,-15.050000190734865,0.004633914800239256,0.0,0.0,3.0944576071324983,92.39597503662108,0.0009238747297786176,1.0693978592201514,0,0.0,False,0.7578055820733243,0.8121350814162596 +03144000,9,69.98062963867187,7.775625515407985,363.4146,05,EastHghlnds,Ref,OH,40.132568,-82.14792,4.418246746063232,23.090933381983834,1.0085443777803407,306.2236160701508,1026.403810586258,0.5928162995901304,4.59500747116037,1.918534635745499,35.741875954968016,1035.1100290766449,0.14428835856166275,0.05994835192180403,0.10482178976825705,0.14993516582133537,0.5638998978993889,9.863538422993189,-15.050000190734863,0.00010452814628653506,0.0,0.0,2.5730045527171375,49.281500488281246,0.0006712629110552371,0.5703877371328847,0,0.0,False,0.8193515843646919,0.8436247475854457 +03145000,5,55.420948974609374,11.084189794921874,345.8997,05,CntlPlains,Non-ref,OH,39.988675,-82.474878,4.702748775482178,24.631962821009306,1.0226085655960595,302.8099544193281,1016.3116344199374,0.5088741337055568,1.5409434603633476,2.129346166752482,30.169060154922,1039.2754606322296,0.21779573846795447,0.11923059351787736,0.09630901936626962,0.16134311325318393,0.5555797469209505,10.251238556852169,-15.050000190734863,0.022210495136044794,0.0,0.0,2.548800022100267,44.226506103515625,0.0005072578205727041,0.511880857679579,1,0.1153986171448825,False,0.7795660662931689,0.8261053345984366 +03146500,25,283.64669079589845,11.345867631835938,1395.075,05,NorthEast,Non-ref,OH,40.059232,-82.339595,18.45155906677246,24.120271407541956,1.0155605912133294,316.3261296542248,1017.9948205010166,0.536322886902468,2.0227228808746744,2.1307338077652656,30.97381286649053,1033.6768854434983,0.18415937327360113,0.11142840591052669,0.10382890455162573,0.1583870482405416,0.5633327453666286,9.978258483066316,-15.050000190734863,0.006449511441915962,0.0,0.0,3.1632103158409186,72.40837377929688,0.0008482771809212863,0.8380598817048249,1,0.0295344368777212,False,0.8401900501981369,0.7743358437565588 +03219500,35,251.46523544311523,7.184721012660435,1469.508,05,CntlPlains,Non-ref,OH,40.419504,-83.197137,13.855243682861328,29.840218720857436,1.055511124057449,297.8015496609448,947.1524695332234,0.4234364570286058,0.9873140306302516,2.08892275480767,21.028748546986208,999.4866076520249,0.18732170979905952,0.14572803532590375,0.11553736165833099,0.1882664481534853,0.5777656311057446,9.856451594321944,-15.050000190734863,0.002461332292582927,0.0,0.0,3.161903495689772,99.89397631835936,0.00027215119916945696,1.1561802814624926,0,0.0,False,0.5676538950739811,0.7880057807735054 +03220000,3,71.09687890625,23.698959635416667,463.9194,05,CntlPlains,Non-ref,OH,40.248395,-83.173803,4.534120082855225,29.037719308139824,1.0656845268203334,316.7006022578523,969.4206476420533,0.4875088517837555,1.1692222317280496,2.368948132770034,23.230400983009343,1032.9442282933194,0.08656099475916758,0.13782185793725904,0.10752913237346962,0.18853205994914632,0.5701252668442366,10.026615891921468,-15.050000190734862,0.0,0.0,0.0,2.5655665183586644,69.30264013671875,0.0010358580620959401,0.8021138904712818,0,0.0,False,0.7269792581830337,0.628465825099837 +03221000,53,491.0345875701904,9.264803539060196,2549.06,05,CntlPlains,Non-ref,OH,40.143396,-83.120466,25.74559783935547,29.613658235905344,1.0602940208820344,301.46939548933113,954.180942348359,0.4477074723973348,1.0119784189304177,2.190500653723092,21.620007281604344,1011.5860020745664,0.16253850755999588,0.1427822774591908,0.11232029523618328,0.1873407249478403,0.5746363961934693,9.928324457767786,-15.050000190734862,0.0015829196361112424,0.0,0.0,3.4016682069967574,137.91074316406247,0.0026961404364556074,1.5961891569914637,1,0.0203623032738094,True,0.7008562916459742,0.8489021332954274 +03225500,21,271.0024414672852,12.904878165108816,1019.562,05,CntlPlains,Non-ref,OH,40.355061,-83.067132,10.625041961669922,25.9675368836602,1.0284216967332525,305.5213715796897,987.1526892156726,0.47977853469403403,1.1093310781228007,2.3969269177369052,26.626641277088023,1014.4596448275746,0.14679383263479284,0.1251880568383284,0.11101369758223675,0.18299646323745636,0.576571621842422,9.763686611501761,-15.050000190734865,0.009768732011030215,0.0,0.0,3.145343004473617,144.76220178222655,0.0009865316096693277,1.6754884465535482,1,0.0481137331202186,True,-0.0569222632597485,0.3611160713913284 +03228300,7,49.27099975585938,7.038714250837054,262.8486,05,NorthEast,Non-ref,OH,40.236174,-82.851289,3.025308847427368,24.027934907855034,0.9902937128050007,347.07769430911225,1029.2762405381231,0.5153707478731174,1.393965442391611,1.8611720786469912,30.285808956606626,1018.986460507779,0.1943814190187902,0.13327979757192135,0.11149401860602834,0.1673488786858987,0.5724990169719311,9.612846616139587,-15.050000190734863,0.0,0.0,0.0,2.431360653423895,36.648769042968745,0.0035553884226828814,0.424175567626953,0,0.0,False,0.7645488494168922,0.8475330339926773 +03228500,13,100.0602999572754,7.696946150559645,490.9968,05,CntlPlains,Non-ref,OH,40.103674,-82.884068,6.409129142761231,24.01739695609627,1.0098494575663939,326.9011636112524,1016.8140325260405,0.5114366147660843,1.365173574993742,1.99371807500798,30.73465927325568,1026.362838026221,0.15321942162930868,0.12060709362229023,0.10695834550855578,0.16938502523148638,0.569066636823124,9.866560571479459,-15.050000190734865,0.011798853140013107,0.0,0.0,2.7335435405346136,65.34828686523437,0.0017328994581475854,0.7563459127920644,1,0.4499852751126348,True,0.6189695848541719,0.7836806378273782 +03228805,5,101.01867053222657,20.203734106445317,325.5012,05,CntlPlains,Non-ref,OH,40.182285,-82.961571,2.5226645469665527,24.228928019443384,1.0397966910908802,279.7852923586494,991.3770406167648,0.4917694859323027,1.2068158528298227,2.3257409137453746,30.941293056114112,1030.7043075894044,0.17814975552592016,0.09662292786394379,0.10245708767248239,0.17689499517132543,0.5667274973906549,10.193140190874157,-15.050000190734863,0.03049835617211242,0.0,0.0,2.695793198877205,85.076361328125,0.0009566053049638867,0.9846801079644097,1,1.2332582086618709,True,-0.7571453091724898,-0.2595700255475237 +04196800,15,127.2343967590332,8.48229311726888,608.067,04,CntlPlains,Ref,OH,40.922833,-83.348812,4.998688697814941,29.712513700221315,1.0496377631233726,274.4648324385721,936.7881164002904,0.4091328418617575,1.133363363053569,2.0348126797098196,21.43227117701782,983.2750263456863,0.14279890542611864,0.1609838951067851,0.1184841175499514,0.21084497705083807,0.5819893468351567,9.908749921969594,-15.050000190734858,0.006029100718537907,0.0,0.0,2.801028506002157,77.14545486450194,0.0009945123456418514,0.8928872090798835,0,0.0,False,0.8054470421225877,0.8436802533362854 +04197100,7,96.42651879882813,13.77521697126116,388.2105,04,CntlPlains,Ref,OH,41.022278,-83.109638,3.8816301822662354,27.39285396188747,1.01003009018491,286.33552971968084,991.5017042249937,0.44364990419829675,1.2110292817327424,2.15130020897254,23.74498135834914,1000.5719671312144,0.11617659065202084,0.17074649182659873,0.12298536397954721,0.17574421343323537,0.5879321193134435,9.546572965941612,-15.050000190734863,0.0011571286055116108,0.0,0.0,2.653068091329235,74.62449853515625,0.0014323573559522629,0.8637094737865306,0,0.0,False,0.782152659805755,0.8394802156844189 +04198000,71,739.0364916381836,10.408964670960334,3243.828,04,CntlPlains,Non-ref,OH,41.307831,-83.158809,31.91657829284668,28.505754332278546,1.0385410106731596,271.0051636153433,960.6043165478841,0.4276115683102939,1.120841393957911,2.145521945794968,23.34348189046351,996.695156530366,0.13101803093388978,0.16887173714266718,0.12079369943039776,0.19007524514068727,0.5866611968198617,9.727478158195815,-15.050000190734865,0.002498055920855211,0.0,0.0,3.5152399724165027,206.07145373535155,0.0010307789780199528,2.3850862700850874,0,0.0005732712349264,False,0.802245447524787,0.8749510811381478 +04199000,21,199.2678445739746,9.488944979713075,947.3742,04,CntlPlains,Non-ref,OH,41.301719,-82.606844,10.886150360107422,24.554201097934193,0.9959306988632766,264.3083716841472,986.080279168424,0.47433584391414374,1.095004191669507,2.0162560894642807,29.09323661430164,981.6008553745394,0.10669236082863037,0.17354996534604744,0.11908298184661831,0.1670482705414993,0.5851617055757948,9.531588908482922,-15.049728548972272,0.015197061549690125,0.0,0.0,2.9710304149545372,92.42422558593749,0.00039596337592229247,1.0697248331705727,0,0.0098123176591202,False,0.7760627130813664,0.7594717982922838 +03157500,25,194.32535400390623,7.77301416015625,1194.712,05,CntlPlains,Non-ref,OH,39.565066,-82.474601,14.188505172729492,24.288528551503344,1.0679560591043435,289.43150388121364,1000.3977044364618,0.5645216368615927,3.0007058036346863,2.1332845108994687,32.57930417422907,1067.9371548864403,0.11505622610339056,0.07904095417582935,0.08850744379899941,0.15449108302626596,0.5504917000580907,10.42908708178335,-15.050000190734863,0.008087153458960116,0.0,0.0,3.084255424593133,74.95336267089844,0.0005267559317871928,0.8675157716539171,0,0.0010408026841746,False,0.807989249832785,0.7615805231741362 +03159500,51,445.4642679443359,8.734593489104626,2447.092,05,EastHghlnds,Non-ref,OH,39.328962,-82.087644,31.5404052734375,23.919920940787826,1.0725007559711268,280.2891627058588,1007.390079580164,0.5886052822012742,4.354387476865239,2.1861552714314323,34.85333847740172,1080.1359967169183,0.11931762577785157,0.05094975827840371,0.08516633382876965,0.14799333020009015,0.54503151017544,10.550071754865474,-15.050000190734867,0.0069804796197872965,0.0,0.0,3.395875897758832,148.58670275878904,0.00021771340107079595,1.7197535041526508,1,0.0163744288603453,False,0.7192193657927992,0.7872844975676058 +03229500,26,304.9044431762695,11.72709396831806,1424.002,05,CntlPlains,Non-ref,OH,39.85673,-82.957126,12.97347927093506,24.188619446249152,1.0273834207626134,296.6718416385193,1003.2335563561458,0.5002211235756949,1.2749660498146733,2.1749582919691117,31.17010476614543,1030.2784175275015,0.15859517570940662,0.10348111289838319,0.10133346512301117,0.17300685670634758,0.5648638129201796,10.17198838351691,-15.050000190734865,0.01727437326628882,0.0,0.0,3.155078047374776,113.08882202148439,0.0005701748887076974,1.3088984030264397,2,0.4290569423142032,False,0.7632920708255606,0.7822896185527404 +03230500,27,306.4439091796875,11.349774414062502,1376.946,05,CntlPlains,Non-ref,OH,39.700618,-83.110187,14.691679954528809,29.941013411449124,1.0731342994189474,298.0741350564316,982.4177692248139,0.4573275502756919,1.024350950929636,2.2580832665728607,21.55960027081206,1054.1455173965403,0.14580035444423475,0.13699534343122985,0.09673798487002672,0.18967972417430212,0.5645249895743427,10.360867417081826,-15.050000190734863,0.00036054027862234147,0.0,0.0,3.1617060098042886,140.0212957763672,0.0008755208691582084,1.6206168492635091,0,0.0,False,0.8053211814013619,0.8034821087091858 +03231500,196,1963.9162637786865,10.01998093764636,9971.048,05,CntlPlains,Non-ref,OH,39.341453,-82.971013,112.1144027709961,27.829642113519455,1.0560942216130909,286.98564812781075,980.840167975612,0.4701733938233156,1.124670743302132,2.290395502429253,24.803548100350323,1035.5110009965708,0.148783507074888,0.12645354357576588,0.0996646972073796,0.18016157559335993,0.5651679397161894,10.28412799516749,-15.050000190734863,0.00639554417232251,0.0,0.0,3.9984379680173396,280.4385693359375,0.0004971002344973385,3.245816774721499,5,0.0700836598583901,False,0.5203773554031226,0.7617785704167636 +03232500,5,57.62402221679688,11.524804443359375,363.2454,05,CntlPlains,Non-ref,OH,39.218399,-83.38547,4.076022148132324,27.98691364609914,0.9900457698335241,309.77000081808967,1062.1349816366128,0.5627371746597213,1.8934021466232456,2.1691101765281755,26.857135326705087,1051.457388250148,0.1682392069205306,0.08252225710679652,0.0851903953272374,0.14793294539018317,0.5378790553678027,11.248218489966927,-15.050000190734863,0.021276006445330186,0.0,0.0,2.5746921419714304,40.9035419921875,0.0018562445184215903,0.47342062490957754,1,0.3303158750215714,True,0.7320893257467731,0.8121545276571208 +03234300,59,633.9064674682617,10.7441774147163,2944.457,05,CntlPlains,Non-ref,OH,39.320342,-82.978235,40.97558975219727,29.538265493872185,1.026250650946683,295.03466047526257,1024.0886341425228,0.4909678761758445,1.7721119943335495,2.2084245213733764,22.63298205499262,1050.4946126148852,0.14444989609609507,0.1116437492661642,0.08620852367076928,0.15855191787957262,0.5449033932107884,11.037202254849142,-15.050000190734863,0.00458627692867987,0.0,0.0,3.4734519695666686,179.43342980957024,0.0007277028635144234,2.076775807981137,2,0.0565849633246189,False,0.6833831173032101,0.839321476351978 +03234500,266,2666.6457406463624,10.02498398739234,13284.22,05,CntlPlains,Non-ref,OH,39.212289,-82.863785,150.39486694335938,28.150993937747465,1.0501285880937754,288.08261969281557,991.3340743980343,0.4777745794550477,1.3582674074289804,2.2680566338671935,24.453537624491666,1040.4561418902329,0.14806367401639375,0.12083629382884292,0.09614206666516527,0.17447529684507784,0.5599965624753945,10.472784821539669,-15.050000190734863,0.00591000181800548,0.0,0.0,4.122587893264477,303.3332800292969,0.0004039878840558231,3.510801852190936,7,0.0668964599602938,False,0.5563585681182739,0.7599996527247915 +03237500,27,198.39546740722656,7.347980274341724,1003.208,05,CntlPlains,Ref,OH,38.803684,-83.421023,15.091041564941406,26.276891162997735,1.0129056343693355,271.518560167985,1082.9384478353963,0.5848861517017481,3.4349832801421294,2.033432004296694,30.159682092545953,1096.8137394404412,0.16211125350669178,0.05647001685908851,0.07700070558691038,0.14183967021651978,0.5285908697083522,11.525225482355888,-15.050000190734865,0.006134025735413856,0.0,0.0,3.019995756040675,90.23883117675781,9.912940731737763e-05,1.04443091639766,0,0.0,False,0.7572056792312327,0.8416701681601693 +03238500,9,125.2629130859375,13.918101453993057,568.5038,05,CntlPlains,Ref,OH,38.858125,-83.928539,9.18589687347412,27.826510014424965,0.9783174983234744,296.7479100558911,1093.5066035289535,0.5194920682901567,1.5939914168326599,2.2139976837931927,27.094016543352833,1069.8386985072991,0.11426690632510118,0.11766063351772939,0.08047863588784403,0.15376554548617904,0.5311988313661611,11.295129365507963,-15.050000190734867,0.00195382140785157,0.0,0.0,2.782473236137503,75.86676171875,0.0032299559097737074,0.8780875198929398,0,0.0059575248879789,False,0.7696912575373316,0.8461318509404578 +03209500,73,645.45571534729,8.84185911434644,3191.611,05,EastHghlnds,Non-ref,KY,37.464268,-82.526263,40.80617141723633,24.99014091521462,1.0362856794074273,583.0000709095972,1126.2996760242877,0.6203471286662097,16.255469350147504,2.177298193283805,37.66020626651583,1166.1324537423347,0.11026471862521632,0.025939862130757257,0.04528250604494773,0.13713053115300716,0.47920381591967465,11.897910283897078,-15.050000190734865,0.0023389532406990453,0.0,0.0,3.5186298541661114,158.72554638671875,0.0003304146812297404,1.8371012313277633,2,0.1055454734139667,False,0.7188414448350016,0.7465239998751156 +03214500,79,652.577588470459,8.260475803423532,3309.055,05,EastHghlnds,Non-ref,KY,37.837319,-82.408758,40.14552688598633,25.2577093156857,1.0086148752500634,505.8052509237664,1136.4854139320944,0.6141711014898343,16.69526035144631,2.144715511521361,37.00882863616718,1145.3715746806754,0.10740968023489994,0.023096758562258582,0.0484343035939703,0.14014332900271942,0.4879414326226328,11.708205785846125,-15.050000190734865,3.615546188342514e-05,0.0,0.0,3.522102042112033,202.0529569091797,0.000259256485151127,2.338575890152543,2,0.0252079288273742,False,0.6151979572763906,0.7705451307738728 +03216500,21,213.97119213867188,10.189104387555805,1036.77,05,EastHghlnds,Non-ref,KY,38.330081,-82.939335,13.466900825500488,25.402006885862946,1.0667008421016242,279.34752762135724,1116.072502058179,0.6273720707221975,8.359292158671323,2.0817453278620395,35.80538074811711,1190.2636672762828,0.09671655927110637,0.016923411856585593,0.05770093356573024,0.14166689759732506,0.5098311692896712,11.79135603453509,-15.050000190734858,0.0047709975022168696,0.0,0.0,3.0177686445402077,70.79683947753907,0.00030050845816731453,0.8194078643233688,1,0.0827400006570585,False,0.8015196730487967,0.7699636552173501 +03217000,13,133.85985662841796,10.296912048339845,625.7565,05,EastHghlnds,Non-ref,KY,38.564245,-82.952116,8.564713478088379,25.70471298998238,1.0312712786480462,291.54818795598686,1129.0870097122745,0.6181646849328818,8.60763493908669,2.0861209834199026,35.427946386399775,1163.3758448390809,0.11193526781204675,0.012191006556687062,0.0649557425820483,0.14100539355877573,0.5120382234273938,11.642196282771215,-15.050000190734863,0.0001270229257333861,0.0,0.0,2.7965920878090835,92.7181947631836,0.0005060002440586686,1.073127254203514,0,0.0016259127137212,False,0.7690936955289501,0.8035191750337749 +03281000,25,295.47389532470703,11.818955812988282,1392.92,05,EastHghlnds,Non-ref,KY,37.555088,-83.593799,20.897541046142575,26.432580950826427,0.9492303842428669,442.770502802949,1234.8855383973312,0.6297327623352881,15.173116442051569,2.340945221983553,34.49450118438761,1170.8543436312407,0.12476239228207354,0.014341515047666856,0.048755973795571526,0.11059868679523972,0.47515710614277706,12.227250962343698,-15.050000190734863,0.0007339564799142556,0.0,0.0,3.1607735097666496,178.84698016357424,0.00031622519600205123,2.0699881963376647,1,0.0729174514565028,False,0.2639908453719766,0.6499952720446075 +03281500,39,361.7611695251465,9.275927423721706,1838.22,05,EastHghlnds,Ref,KY,37.47981,-83.675192,26.48676300048828,26.571960251834813,0.9591843628767783,370.9707337690888,1231.3155653824424,0.6294380192397161,12.135976624293738,2.1932501387423455,34.26087226971506,1180.6348661117197,0.12634071807014127,0.013310789159484639,0.04748526982089396,0.11240233932096902,0.47754883637085166,12.401091044393075,-15.04993824476955,1.2671615163525653e-05,0.0,0.0,3.2871842877135506,142.82576858520505,0.00013737847621086985,1.6530760252917251,0,0.0,False,0.8521639287822506,0.8722566706564722 +03282000,137,1430.780677154541,10.443654577770372,6883.035,05,EastHghlnds,Non-ref,KY,37.555364,-83.768251,99.93180847167967,26.138366263256184,0.9793834417679763,404.1672137052864,1203.9263661504137,0.6252383026558404,13.594553881541382,2.245446066742372,34.96173961949754,1177.6887748625577,0.12256255449125365,0.016721142805093243,0.04755460684853019,0.11742882051489024,0.4786432275850103,12.366980374863093,-15.044323726867475,0.0006171563717749815,0.0,0.0,3.83800885173381,286.53783105468744,0.0006527386722154915,3.316410081651475,3,0.0356947518638185,True,0.7061527728737327,0.8341365884450804 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+03098600,51,445.04377038574216,8.726348438936121,2525.609,05,NorthEast,Non-ref,OH,41.105058,-80.662576,30.817028045654297,21.46340628294107,1.0199404723455705,320.9629992932264,994.0250246270614,0.55146442089941,1.8074897084414483,2.163145645053493,37.21057007423911,1013.0520144060313,0.17325894209033144,0.07072248339734526,0.11884054419040668,0.16081929441600115,0.5846320584965805,9.349520011488105,-15.050000190734862,0.02564808354999127,0.0,0.0,3.405770865496526,135.42610528564452,0.0006967607187107205,1.5674317741394044,5,0.3071881576424091,False,0.7295983844437157,0.8560094602154014 +03105500,109,992.1364130325318,9.10216892690396,5752.941,05,NorthEast,Non-ref,PA,40.888674,-80.337008,80.00794219970703,21.354192616753213,0.9902289794246784,333.43105065346697,1020.3050781048186,0.5591405276242915,2.2173557855029546,2.2606459725691566,38.956685352886886,1008.0077401042367,0.16473253677703764,0.07173086932712812,0.12113714915235665,0.15991221183437235,0.5861514112413035,9.102198837711379,-15.050000190734867,0.02465344775836523,0.0,0.0,3.7660065646317435,186.24032746124266,0.0002469964965712279,2.1555593456162345,7,0.2264379247903419,False,0.7708609787705303,0.8390613883254281 +03106000,19,168.84433947753905,8.886544183028372,921.4443,05,EastHghlnds,Non-ref,PA,40.817009,-80.242284,12.189705848693848,21.835532082203027,1.0114695518569847,369.5187623513056,1021.787758041114,0.5961301308731785,5.364295994115762,2.078991089985438,42.42548277911854,1033.355702454981,0.19642777091969593,0.028673594843366196,0.11653345062161187,0.14406166354721123,0.5765191112655167,9.302684212699356,-15.050000190734863,0.003598778641130715,0.0,0.0,2.9656515223894537,76.13970617675781,0.0003002745215781033,0.8812465992680303,0,0.0,False,0.8679344691611803,0.9249706582114192 +03106500,19,183.1543314819336,9.639701656943872,1043.36,05,EastHghlnds,Non-ref,PA,40.883952,-80.233673,15.1193208694458,22.033576235283537,0.9637939817610015,399.39090590139864,1057.838853700461,0.5670785724683074,3.8766935935224214,2.181129992131126,40.288444798335945,1018.943253396925,0.15852753558097318,0.05570476236210262,0.12699275489875586,0.15175946201391408,0.5833518672359885,8.778326275107615,-15.050000190734862,0.009324687763075568,0.0,0.0,3.0159329608569463,74.18736279296874,0.003351430408656597,0.858650032326027,1,0.096623506394184,False,0.7656371272000295,0.7612355009840929 +03107500,151,1408.0653007888793,9.32493576681377,8034.329,05,NorthEast,Non-ref,PA,40.763398,-80.315063,108.96856689453124,21.51241187109792,0.991350538632285,346.59029392472996,1023.9389755594075,0.5655205723968967,2.9155157687543447,2.2548750491426355,39.624686313257385,1013.0955002461012,0.16770332997697718,0.06315944771788524,0.12092826257604018,0.15670691128708636,0.5842219319620602,9.102262261262066,-15.050000190734863,0.019427595920613386,0.0,0.0,3.908058531811421,204.54026105499267,0.0008527113823220134,2.3673641325809336,8,0.1815347519321485,True,0.7875727119655558,0.8023309362185544 +03108000,11,92.7683955078125,8.433490500710228,463.4865,05,EastHghlnds,Non-ref,PA,40.627844,-80.337563,5.173525810241699,23.08651658295842,1.033179888076732,339.2528534315784,986.9802106537792,0.5939726764336294,6.120011044308218,2.026875584340484,39.2552463171386,1019.4047081699117,0.15429832724916734,0.006150657140853844,0.0981700035082765,0.1443167986257769,0.5610590696195845,9.871701944695031,-15.050000190734865,0.006097182082433699,0.0,0.0,2.678431458347939,70.07969873046875,0.0012291149469092488,0.8111076241952402,1,0.0855323448102569,False,0.8480028525591743,0.8157205944995942 +03109500,29,258.0935424804687,8.899777326912714,1304.177,05,NorthEast,Non-ref,OH,40.675897,-80.540624,15.125432014465332,21.376056319521624,1.0522009153822396,351.4403229673795,980.4904157796555,0.5834994089378918,3.6013979170684776,2.0850548573966012,39.36933418964038,1031.6028502692993,0.185416108235609,0.08689876209219398,0.11364607132382314,0.16750470351381572,0.5803242041046078,9.501042132654227,-15.050000190734858,0.004807143201116651,0.0,0.0,3.123446520177737,84.7755078125,0.0016795950941741467,0.9811980070891204,0,0.0046161803104852,False,0.6962296155410046,0.6747790387323441 +03110000,11,77.33990237426758,7.030900215842507,381.4083,05,EastHghlnds,Non-ref,OH,40.537842,-80.725078,4.3823442459106445,22.7005696252679,1.020622230916583,348.4381326610186,1001.777590372805,0.607679583487528,6.784815768783436,1.8731679588912789,37.81611235827507,1022.3437216930325,0.1981711136753101,0.020742223121466907,0.10579889351911777,0.164401746050031,0.5633140622703992,9.73806237037123,-15.050000190734863,0.0,0.0,0.0,2.5854865741942388,48.21985424804688,0.002221214585006237,0.55810016490795,0,0.0069278098595673,False,0.7925612829624478,0.807581016533186 +03111500,7,67.8085869140625,9.686940987723217,318.8142,05,EastHghlnds,Non-ref,OH,40.193404,-80.734248,3.5075199604034424,23.384689751267732,1.0083686694543668,344.9689023508495,1010.7517808574316,0.5992480369553881,5.185939023293354,1.9660076420432957,37.94939444607814,1019.1027836710671,0.1684475978434963,0.021340244256961582,0.09668866246849658,0.1430002981141151,0.5554413088474255,10.076690143456435,-15.050000190734863,0.0012580909904382077,0.0,0.0,2.516851494813751,40.373244140625,0.0020084651187062263,0.4672829182942708,0,0.0,False,0.5670374968662271,0.6980490145067659 +03111548,3,53.516173828125,17.838724609375,253.1448,05,EastHghlnds,Non-ref,OH,40.067016,-80.80842,3.3223800659179688,23.38744110631694,0.9896135869743252,341.6598556695038,1025.4994904550047,0.5931795490675851,5.661906362074308,2.176570922874932,38.34865504504071,1014.7443420637961,0.08314807521602793,0.0503684537117773,0.09315233355853597,0.13462759422118736,0.5532717149289648,10.071320763737202,-15.050000190734863,0.003889839749408694,0.0,0.0,2.456522425982423,48.6696708984375,0.00238427915610373,0.5633063761393229,0,0.0,False,0.4808629835405705,0.6118648657326331 +03117000,37,248.45397747802733,6.714972364271009,1336.8,05,NorthEast,Non-ref,OH,40.770336,-81.52401,14.478141784667969,20.770833953273335,0.9952330491780832,324.4074233080891,974.1720667135179,0.5574405240620989,2.1917680755498608,2.0589462732666393,39.15092070047249,969.4011433258831,0.16045190512741536,0.08458144343202474,0.12001697343855385,0.17137487521429492,0.5801240746423096,9.556726805804086,-15.050000190734869,0.008139068806644856,0.0,0.0,3.1651976236903505,85.01567199707031,0.0003392269427422434,0.9839776851512768,1,0.0714926848767445,False,0.7005100314367962,0.8346787427161716 +03117500,17,124.557892578125,7.326934857536765,656.2251,05,EastHghlnds,Non-ref,OH,40.672561,-81.259831,8.296869277954102,21.595905226591775,1.01692770899734,343.2574602021706,996.6289803602931,0.5734494070559216,3.6664230707300955,1.9873060397470161,38.87551029801826,1013.241113209398,0.17914708517272324,0.11221304241843255,0.11438764913466135,0.16710034171234145,0.5762483614041806,9.562551032831848,-15.050000190734865,0.0023249362754223404,0.0,0.0,2.8359857556702477,45.459010742187495,0.0008613418322056532,0.5261459576642071,0,0.0,False,0.7807093324923899,0.8315576789587928 +03118500,11,90.66739770507812,8.242490700461648,453.5037,05,NorthEast,Non-ref,OH,40.734226,-81.352058,6.950320243835449,20.995284553153216,0.9716649148577635,344.0100333178848,994.8155231074044,0.5718110295075753,2.1092739713291517,2.029141505118208,39.32381629051831,966.5823355781301,0.18288022028663822,0.10158343241920453,0.11721913898171274,0.16197729999616592,0.5775215714292572,9.587251840950413,-15.050000190734862,0.008819998851224099,0.0,0.0,2.7009472797537297,53.16694189453125,0.0014349871780723333,0.6153581237792969,0,0.0,False,0.8025807820401195,0.8925973396890513 +03124500,15,157.60085192871094,10.506723461914062,803.0898,05,NorthEast,Non-ref,OH,40.587562,-81.523175,8.038488388061523,22.006297013692585,0.9796205981920391,336.4467730767796,1003.2079986575114,0.5804747962912532,3.34830331690322,2.0614562333397686,37.58442181476909,982.6244497007499,0.20632177748213046,0.10719217371539425,0.11259651436886896,0.18206668236281925,0.5730412976740157,9.616189367978087,-15.050000190734863,3.715764426871281e-05,0.0,0.0,2.8979358332737353,58.45224365234375,0.0017281781183555722,0.6765305978280527,0,0.0077478278443203,True,0.6191816433700629,0.7378009326777171 +03129000,137,1180.9313668518066,8.619936984319756,6319.039,05,EastHghlnds,Non-ref,OH,40.261459,-81.60901,77.55934143066406,21.95669863359873,0.9954416342682968,329.6526863799887,1004.8057981988869,0.5791042394421521,4.322459162607085,2.1663133272222823,38.296434989751994,1000.1424914798702,0.17555844520185973,0.06489961063129965,0.10894460805619509,0.17006701960478407,0.5706083557730257,9.740851637516895,-15.050000190734865,0.008787876492571754,0.0,0.0,3.8014877912109917,188.90168457031248,0.00028455170104280114,2.186362089934172,8,0.2435983337127016,False,0.5737825566509669,0.7897897594421276 +04206000,21,195.6015202636719,9.3143581077939,1047.329,04,NorthEast,Non-ref,OH,41.135611,-81.547062,13.309860229492188,20.968923145121202,0.9366701896455579,345.9862006064437,1031.9725590582873,0.5400825177264967,2.1853808799436307,2.142711519886835,38.896200998584135,963.0200168879787,0.13476912176662054,0.05350748508466696,0.1306989031423068,0.14052155112695208,0.5849742715636289,9.163970068023623,-15.04999218769765,0.02433793762525759,0.0,0.0,3.0063673519698595,93.16569580078125,0.0005523276049643755,1.078306664360894,2,0.0850924472175393,False,0.7674748617786149,0.881329565368508 +03151400,3,60.97783984375,20.325946614583334,290.7306,05,EastHghlnds,Non-ref,WV,38.743434,-80.525367,6.769615650177002,25.134055207422858,0.8123051627088734,506.38351658951524,1351.5175039400672,0.629883542337371,11.330051943752103,2.1779467557045935,34.925336461655846,1090.4333963823967,0.17367455236715992,0.0463671345218916,0.08111942574613794,0.13503190380191973,0.5118816645240717,10.298194096154129,-15.050000190734863,0.0016774847192850217,0.0,0.0,2.4848775484103625,45.28229296875,0.0022871263790875673,0.5241006130642362,0,0.0,False,0.7943175493090576,0.7835817988727194 +03155000,77,828.5708259429931,10.76066007718173,3924.7,05,EastHghlnds,Non-ref,WV,39.058969,-81.389564,62.796974182128906,24.508488176925127,0.9776023656324302,336.1832610468676,1189.8710838185552,0.617838447577168,11.598997728245964,2.182342281976127,35.70778327075602,1159.6771473436872,0.17249037832344846,0.026131126291345657,0.06830126377543563,0.12864336678917712,0.5179904332158899,11.205234721053268,-15.050000190734862,0.0012984641801185855,0.0,0.0,3.5940254467616866,232.04754748535154,0.00011661792814265937,2.6857355033026797,1,0.0096425316000919,False,0.8131999471159675,0.7395369114926369 +03159540,9,108.11648486328124,12.012942762586803,400.851,05,EastHghlnds,Ref,OH,39.063689,-81.881802,4.554289817810059,24.12726999135363,1.0910496333364237,245.31410486878366,1045.1688353244829,0.609428636466729,5.6180695048816425,1.95598878034807,36.62978003448894,1140.3015159031377,0.12498071955772914,0.01674221274226942,0.07032853964011355,0.13053286189969407,0.5308204874790995,11.335447136670565,-15.050000190734863,0.00577724470444624,0.0,0.0,2.6167636744388187,57.90851513671875,0.000418071576859802,0.6702374437120225,0,0.0,False,0.7598210187296792,0.79581723661539 +03194700,13,175.25295855712892,13.48099681208684,689.0697,05,EastHghlnds,Non-ref,WV,38.597326,-80.490364,19.880352020263672,24.94716536326097,0.6699820079531592,950.7342526201735,1495.4910883171706,0.6142240507948743,15.073581049978436,2.149877640740047,35.02433917748783,992.5759711565305,0.15978329193134608,0.1146852129402337,0.12295955414811745,0.12473836214693042,0.5099966985115105,8.713709636607126,-15.050000190734862,0.0001950065533146292,0.0,0.0,2.861905815749207,108.08487744140626,0.00384150049649179,1.250982377794054,0,0.0,False,0.8073765637660593,0.7871007893968515 +03176500,199,2115.928261077881,10.632805332049651,9781.729,05,EastHghlnds,Non-ref,VA,37.372899,-80.860635,133.71437072753906,24.486524062722093,0.9848831861646157,816.6146044638471,1112.8572799759745,0.6444644263207635,9.521239957829474,2.233124536833077,40.98917013949201,1077.9661102869347,0.1489904938580866,0.031213193870264805,0.058434023079783064,0.10879799197955187,0.4782856107511681,10.61869105764094,-15.043451592410689,0.003454958483812254,0.0,0.0,3.9908920377090866,428.7673911132813,0.0012012316146865487,4.962585545292607,1,0.0711180875587168,False,0.6698709371863836,0.7840112881474559 +03179000,27,225.57951892089844,8.354796997070313,1024.424,05,EastHghlnds,Non-ref,WV,37.544008,-81.010368,12.361068725585938,24.75638852306573,1.0347385039485546,766.2188596677934,1037.5001883367456,0.6195397849989437,11.130394409996622,2.026104938648598,39.807187778428386,1065.6011889539445,0.14371791861989253,0.0469018827030605,0.06765942739866551,0.1430754146599678,0.4864696163207981,10.533866311921743,-15.050000190734867,0.0005455138244932894,0.0,0.0,3.0196489082521727,117.86970654296874,0.0036215931177139282,1.3642327146176938,0,0.0018932127936673,False,0.8143743993556591,0.8125769603967559 +03184000,95,821.3504316101074,8.6457940169485,4225.824,05,EastHghlnds,Non-ref,WV,37.64012,-80.805083,66.04076385498047,24.898254702128664,0.9524954687613947,808.0397924480845,1124.9894007801406,0.6211698213411849,10.875122544171317,2.1942022543314796,37.97513974323404,1054.9637763629423,0.12863382334559637,0.05152712989825613,0.10171142960249618,0.10975979785008652,0.5119582623074844,9.538755079959921,-15.050000190734862,0.0009367352015362302,0.0,0.0,3.6253954277861435,279.65192578125,0.0005037085502408445,3.2367121039496527,0,0.0002770287464844,False,0.8297761780429396,0.8958085600320532 +03185400,379,3604.380896881104,9.510239833459377,17348.47,05,EastHghlnds,Non-ref,WV,37.955113,-81.076488,237.57095336914065,24.6815951421634,0.9852678841989756,798.6450674155159,1103.0811661728662,0.6340638097964447,10.098718771960534,2.229000708426789,39.8494740695157,1069.8777634869805,0.13946919114313477,0.037914295969001796,0.07087979908216757,0.11575881966788441,0.48927608031586917,10.323222623751947,-15.04630474624973,0.0034618871826000356,0.0,0.0,4.239373707873343,547.4605529174805,0.0016901100752875209,6.3363489921004685,2,0.0459420379497809,False,0.658923028827135,0.7987935578239529 +03189100,21,273.64154309082033,13.030549670991444,1369.11,05,EastHghlnds,Non-ref,WV,38.290945,-80.640924,40.73240280151367,24.278058059792443,0.69749889522209,1002.5920115637614,1452.453495951851,0.6171404250569177,12.790215378627504,2.1873161038461384,36.398811894948636,1005.7149089158045,0.08147863862739475,0.12674511956752582,0.12065078057993855,0.148320210885669,0.5104970267285651,8.663280066405099,-15.050000190734862,0.0,0.0,0.0,3.141060003621726,89.7941416015625,0.003958142362535,1.0392840463143809,0,0.0,False,0.8032080329885806,0.8253321974619845 +03190400,21,172.36273840332032,8.207749447777157,944.6616,05,EastHghlnds,Non-ref,WV,38.189832,-80.946766,20.26327896118164,25.154386531823977,0.8192362834865079,847.8212211851477,1274.3625827673732,0.6162450566522427,10.330110789551352,2.1068843011057528,36.55375759626481,1035.8000416428697,0.07462394110418326,0.07414946191323266,0.09431453202844221,0.13778707868477466,0.5086481247697272,9.374988702644348,-15.050000190734867,0.0032608931110196552,0.0,0.0,2.974501313798919,86.16775048828124,0.014644383452832699,0.9973119269476997,0,0.0,False,0.7234185439704734,0.8363598767605471 +03192000,61,655.2662820739746,10.74207019793401,3407.807,05,EastHghlnds,Non-ref,WV,38.23344,-81.180942,80.73761749267578,24.76481387396733,0.7803130832254436,845.7879567508529,1346.258818589212,0.6184155434696951,11.804597874425989,2.2281632378414575,36.301148587557094,1037.5446288027,0.10037883384636992,0.08716659917878737,0.0995489451869251,0.1492883928258582,0.5082234005618318,9.33953293597279,-15.050000190734863,0.0020153967879064905,0.0,0.0,3.531728428356171,176.90964013671876,0.0010163785191252828,2.0475652793601706,1,0.0936837453297476,False,0.5457389940366086,0.7282118348996409 +03193000,461,4430.621101348877,9.610891759975871,21702.95,05,EastHghlnds,Non-ref,WV,38.138163,-81.214274,345.2035522460937,24.71712516232474,0.9503430635862622,796.3525072173752,1144.7398661378645,0.6308254085249222,10.483402691764026,2.2435203656500025,39.137842421752524,1065.6316799089545,0.1323603944137353,0.0458436340681095,0.07513036469866895,0.12319567932268782,0.4928418695950695,10.188637791951239,-15.04704741892006,0.003235748202540511,0.0,0.0,4.3368105036176,601.5383678588868,0.0008885616553016007,6.962249627996374,3,0.0550861822642864,True,0.6367544825172808,0.8014341002721435 +03197000,57,631.2714646148681,11.07493797569944,2950.136,05,EastHghlnds,Non-ref,WV,38.470932,-81.284006,60.52684020996094,25.140439682101103,0.8382998478578418,588.1429916760519,1332.9460651311401,0.6174789508855867,13.998071746226985,2.262913066953613,34.79082531719758,1099.3028813545754,0.15767035834492893,0.054821533396481166,0.08047233035409034,0.137194336589495,0.5071775957214782,10.380576897534388,-15.050000190734865,0.00041846751879200827,0.0,0.0,3.4769102566808883,267.07221972656254,0.0003689895092975348,3.0911136542426223,1,0.0407958612249429,False,0.5686108457891372,0.6734051143114542 +03198000,571,5491.351882400512,9.617078603153262,27095.16,05,EastHghlnds,Non-ref,WV,38.371484,-81.70207,432.7264099121094,24.824261139250137,0.9404664679225535,737.5092998889598,1166.853726783624,0.6273122674296426,11.185046906073946,2.2500666677422965,38.28208827088196,1075.416437301646,0.1338579034120478,0.04519624547087396,0.07396763772873764,0.12720676174356232,0.4952555110268528,10.334612004333739,-15.047633873281976,0.00303797790196953,0.0,0.0,4.4329673834410075,665.8835047302247,0.00024412503989879042,7.706985008451675,6,0.0525401236512195,False,0.6205730352538061,0.7732979375640969 +03198500,19,180.5585196533203,9.5030799817537,1013.508,05,EastHghlnds,Non-ref,WV,38.179822,-81.711511,14.534977912902832,25.22251245915446,0.9235732507313076,486.9593898144411,1194.4961885654393,0.6187189428383365,16.47066610829467,2.2392914120888965,36.386889802164056,1100.3867761524168,0.121054679909039,0.02141398486397711,0.06092044517512852,0.14471972952258344,0.49912592541030626,11.131426543072028,-15.050000190734862,0.0017624359837800248,0.0,0.0,3.0305114008954135,113.42195544433595,0.000510024547111243,1.3127541139390735,1,0.0265952562727893,False,0.7506852848109594,0.7717905756201171 +03200500,41,414.21514642333983,10.102808449349752,2223.267,05,EastHghlnds,Non-ref,WV,38.338983,-81.841518,31.90648651123047,25.109312465972415,0.9430110517396414,449.04198971823877,1191.1214777372986,0.6130318216772975,15.927916642701271,2.250694966106702,36.35910198822786,1121.1725479421136,0.11868035621369726,0.019630956560470224,0.05671805565128326,0.14448773583987481,0.4984361270387607,11.34972397902161,-15.050000190734858,0.0015360251572463179,0.0,0.0,3.354260786564969,141.15928942871096,0.00020691832469310611,1.633788072091562,2,0.0606637108003582,False,0.7622629547856183,0.8476502232096046 +03202400,19,175.01557522583008,9.211346064517372,792.7677,05,EastHghlnds,Non-ref,WV,37.604,-81.64511,10.957404136657717,24.788529877356364,0.9112832579506203,702.02791747182,1190.663268198305,0.6293758995466199,14.171277116884427,2.0319610179129857,37.63399981696959,1083.2900357544559,0.146170709221072,0.03954400977230857,0.06231619892176121,0.1457439454012911,0.49135465727304567,10.657229672711349,-15.05000019073486,0.00011916509279392204,0.0,0.0,2.904125439648479,78.59668313598632,0.0017934022471308708,0.9096838325924343,0,0.0205606456295059,False,0.7945097778096102,0.8712798737021641 +03202750,3,71.5261103515625,23.84203678385417,327.1086,05,EastHghlnds,Non-ref,WV,37.623166,-81.707334,5.374424457550049,25.31376478286143,0.8701990298116975,624.2741576889,1253.0112952819004,0.6108309553149134,18.40423849176825,2.210092616246405,36.61438059794995,1089.103974535635,0.08179301255288235,0.040380643943973664,0.0659845761114004,0.13859054731175388,0.49561924941834623,10.780567818472104,-15.050000190734865,9.022567135404646e-05,0.0,0.0,2.5229039903685675,55.617958984375,0.001822896534577012,0.64372637713397,0,0.0,False,0.8268300476699821,0.8883123816669428 +03203600,45,451.2170594787598,10.027045766194664,2158.022,05,EastHghlnds,Non-ref,WV,37.842326,-81.975957,30.46009063720703,25.138642690651185,0.9244051793581197,596.9574298901258,1200.0588561879367,0.6200835583684307,16.98267194051277,2.148764400388592,36.858770977213574,1107.190748157377,0.12041586722613898,0.03177858487159336,0.058426606399024576,0.1467641931259063,0.4922278559905308,11.044047487660713,-15.05000019073486,7.513481304476318e-05,0.0,0.0,3.334657382493179,160.8210356750488,0.0001266190956812352,1.861354579572324,1,0.0528236284633862,False,0.3704724470306947,0.6238678213726341 +03207800,19,143.34277684020995,7.544356675800524,768.5802,05,EastHghlnds,Non-ref,VA,37.35372,-82.195693,10.958368301391602,24.986183519469122,1.0276840925742676,621.6719131351866,1118.5147691318343,0.6213467849582613,17.02118559876763,2.1001806139959607,37.453833773340314,1149.107388525851,0.09873395824164319,0.03167004504665232,0.04859557584640928,0.1385355016482026,0.4781431961555267,11.5058158558717,-15.050000190734862,0.0,0.0,0.0,2.919360770133218,80.62234130859375,0.002301843138411641,0.9331289503309462,0,0.0,False,0.7302274047871273,0.720294506351504 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+03032500,31,243.10210665893555,7.842003440610824,1376.622,05,EastHghlnds,Non-ref,PA,40.994509,-79.394209,26.783679962158203,22.492143347343152,0.8556346495886467,476.0970243148282,1126.0594582921694,0.588203165359891,6.147024435451914,2.164459693966271,40.01846686849409,963.1447524888688,0.1820140040940745,0.050383339243992346,0.15304384504927476,0.14536610150170878,0.5909771798982392,7.94983672363202,-15.05000019073486,0.00113711214593293,0.0,0.0,3.1522968595355394,131.34210546875,0.002183629432693124,1.520163257740162,0,0.0001562139936079,False,0.8420654082315407,0.8862807128447994 +03034000,11,102.8862254638672,9.353293223987926,407.5902,05,EastHghlnds,Non-ref,PA,40.939231,-79.008367,8.006470680236816,22.412969233798094,0.8680602776050491,478.38970474657395,1121.2253703264842,0.5867176724717479,6.103186931175038,2.0984444561992843,40.71834457872579,972.7959202564847,0.13681188079624887,0.047944192218251525,0.14735113309382689,0.1496405045375071,0.5847972465265886,8.080285100293754,-15.05000019073486,0.00040812869996084065,0.0,0.0,2.7262099902556063,72.90543798828125,0.0007454409496858716,0.8438129396791811,0,0.0,False,0.6797395000865998,0.6986802168191217 +01557500,3,16.126789306640624,5.375596435546875,115.2963,02,EastHghlnds,Ref,PA,40.683673,-78.233624,2.0624070167541504,22.89787134151784,0.8817332452705815,574.292960017423,1104.3529889808913,0.5980946490249108,6.78271549706225,1.8410698825233003,38.38985600683614,972.2631118642129,0.21730264761131232,0.008906706368611874,0.14883266464721961,0.12179978740447577,0.5933562711817462,8.647205726981623,-15.050000190734863,5.940401890698429e-06,0.0,0.0,2.142621916844314,13.02718701171875,0.005917874630540609,0.1507776274504485,0,0.0,False,0.7605063385435672,0.8120296656521495 +01560000,13,81.45323583984376,6.265633526141827,444.6405,02,EastHghlnds,Non-ref,PA,40.071747,-78.492517,7.993641376495361,23.25212090977821,0.9924902980417822,496.0890261153905,1012.4153579929894,0.5855523958371068,7.5007727829932564,2.00055874091051,38.125447861428064,999.234716911484,0.14295279593567253,0.051237899734267955,0.12456466936193718,0.11779677460777317,0.5702993628901275,9.289296137769853,-15.050000190734865,0.0007247168149027827,0.0,0.0,2.7066649942535457,48.37854736328125,0.0004698677221313119,0.5599368907787182,0,0.0,False,0.8643646665031908,0.8642444133067988 +01601500,19,116.31352746582031,6.121764603464227,641.3698,02,EastHghlnds,Non-ref,MD,39.669611,-78.788028,10.60965633392334,23.723068411206746,0.974149781934994,551.1678341956788,1034.3718554768814,0.5897520989489335,9.54974087441599,1.9061938148764286,38.45400771948099,1003.8160613160859,0.1316512100666248,0.03300313550053167,0.12474953218438234,0.1115231586062159,0.564826950300434,9.038594236412086,-15.050000190734862,0.00043669830157503893,0.0,0.0,2.8170579483027804,54.26349291992188,0.0031057295855134726,0.62804968657317,0,0.0,False,0.8445604838918701,0.8900523480026907 +01603000,41,403.2832428588867,9.836176655094798,2267.205,02,EastHghlnds,Non-ref,MD,39.621806,-78.773417,42.50309753417969,23.836778189921024,0.9276789118697272,656.1741467230186,1087.4266333541273,0.5896269627681865,8.853640266894667,2.231349823167809,38.53795193683702,997.970375168994,0.17171865754287757,0.036393823170382957,0.12476196995862303,0.12029031740201623,0.55557607619465,8.69685111761042,-15.050000190734863,0.004779254503457322,0.0,0.0,3.3576051333911288,144.96416918945314,0.0009693542961031199,1.6778260322853373,3,0.1645672892961771,False,0.7878070896109172,0.8461533998992183 +03036500,499,3995.94185609436,8.007899511211143,23225.2,05,NorthEast,Non-ref,PA,40.820342,-79.531434,441.5108337402344,22.1733388798406,0.8243615409700743,497.9783839789475,1148.8648772164809,0.5870734081517743,6.169348571321919,2.2358697802348106,39.02662508767563,944.9841615406913,0.17184034828374317,0.08896730094760702,0.16437032040523808,0.14267834521920592,0.6018609988227476,7.514536348387403,-15.050375877793403,0.00869264932631594,0.0,0.0,4.366435831565264,479.2044457092287,9.359020623378456e-05,5.5463477512642205,12,0.0794837241651108,True,0.6485405474702198,0.7862252681605217 +03038000,11,109.51375323486327,9.955795748623936,493.6905,05,EastHghlnds,Non-ref,PA,40.654787,-79.348651,7.429296016693115,22.14681896742536,0.9117811340501243,389.1890385697854,1122.3365845295882,0.5773637264545374,7.33784593997428,2.117459509489556,41.74418908465301,1020.4848999736364,0.16860718060518337,0.027862687154898598,0.12148205144384665,0.13316475862491398,0.5741484890200037,9.003025426141198,-15.050000190734862,0.005017427602763743,0.0,0.0,2.780418175552793,63.84462890625,0.0011110116029158235,0.7389424641927084,1,0.1401437974812203,False,0.6581988808871795,0.4721570508878279 +03041500,53,331.2596676025391,6.250182407595077,1863.592,05,EastHghlnds,Non-ref,PA,40.419238,-79.02614,38.3320541381836,23.125344117269687,0.8411100792049024,632.9520877485669,1125.2339116555816,0.5812170575522644,6.5658699709912565,2.075448970394604,38.98609588158169,943.9802405266715,0.15536477531239717,0.038129515067945007,0.13655127328363836,0.10196404636702132,0.5690391173409498,8.228129757095319,-15.050000190734862,0.005929505947845378,0.0,0.0,3.3004198678358208,107.54141772460937,0.0011714532738551497,1.2446923347755714,2,0.0682109183579593,False,0.890835604282665,0.9343138909267026 +03042000,9,93.74875341796874,10.416528157552085,499.2651,05,EastHghlnds,Non-ref,PA,40.473402,-79.183367,10.477417945861816,22.741816201994578,0.8152784722726631,558.9743922450054,1183.5789344969498,0.5700117502415823,5.211492362461213,2.10033265299109,39.07403296214423,963.9369642312848,0.18193934337842707,0.015783655899730787,0.13520853555742704,0.1032182281779184,0.5716726850625617,8.358244440037556,-15.050000190734862,0.003217880811890348,0.0,0.0,2.6993351807219024,59.881585693359376,0.006445528473705053,0.6930739084879557,0,0.0,False,0.8313736406666896,0.8289704734079253 +03042280,3,35.9838740234375,11.994624674479168,154.4994,05,EastHghlnds,Non-ref,PA,40.572567,-79.103366,2.8742423057556152,22.45590787757377,0.8192305102945582,492.07812291827037,1194.9531470575253,0.5782914269525503,5.289963150314913,1.9232620476237383,39.7219547444152,978.5870528765173,0.19583519654652715,0.008550622464083195,0.13055978223558348,0.10772639495432738,0.5702749139609382,8.583907408925658,-15.050000190734863,0.0029309957323143897,0.0,0.0,2.2329012214647803,27.754310546874997,0.004156072624027729,0.3212304461443865,1,0.1897280087206172,True,0.8000964081433317,0.8851142110862997 +03042500,11,91.36979467773438,8.306344970703124,440.8776,05,EastHghlnds,Non-ref,PA,40.51729,-79.171701,7.873174667358398,22.404673457855004,0.832444643125576,470.2086027567587,1187.9893803082473,0.5754708958441338,6.380368898530833,1.9370210675623647,40.38112744108563,988.4487310411771,0.17116622441803545,0.012849544306318466,0.1293656120816239,0.11145989704022223,0.5714041385522005,8.669992613260701,-15.05000019073486,0.001208188878791042,0.0,0.0,2.6490194035933654,47.5573720703125,0.0022414899431169033,0.5504325471100984,2,0.1446234382901292,False,0.8000739389020867,0.8892748717744481 +03045000,11,67.41436682128906,6.12857880193537,444.0222,05,EastHghlnds,Non-ref,PA,40.29257,-79.340591,8.058368682861328,23.276208887151945,0.7823561563291631,532.7781983321811,1241.1978313713662,0.6059826543400779,7.150245212071373,1.9582289897192127,37.33612429706649,966.6538804410634,0.14903942552256666,0.009083772434695995,0.11608431472932114,0.11162666243696831,0.5600018439973891,8.53674871510889,-15.050000190734865,0.0028547089467330557,0.0,0.0,2.655806033496028,29.34828503417969,0.0036741415970027447,0.33967922493263525,0,0.0,False,0.7959839663023369,0.8516795114610224 +03048500,113,841.0833526000977,7.443215509735378,4728.982,05,EastHghlnds,Non-ref,PA,40.60451,-79.551989,90.13954162597656,22.98921809071821,0.8599294132825009,530.5146237686384,1142.5712511459085,0.5824149771182142,6.494013661215139,2.142231599608575,39.25040519181139,977.4034565231234,0.16280253127425914,0.02321721442348318,0.12540727487927456,0.11430542110764788,0.5670576517664889,8.624268024625449,-15.050000190734862,0.0059399125387197245,0.0,0.0,3.6751678553295934,189.239697265625,0.0005394099862314761,2.1902742739076966,5,0.061442151684136,False,0.184833901240746,0.5909605227400451 +03049000,7,61.9026025390625,8.843228934151785,357.1533,05,EastHghlnds,Ref,PA,40.715898,-79.699494,4.687828540802002,22.175542341208736,0.9887758733912846,380.37820813550974,1054.802887095615,0.5741823107663304,5.87957292164173,2.0208375137427677,40.898279133572046,1042.6813787297756,0.14552987617739938,0.03462943885031218,0.12140684647111057,0.1456380868988224,0.5811582032878101,9.004113454559253,-15.050000190734863,0.0004013429698136026,0.0,0.0,2.558580464839102,45.8593134765625,0.0024508049245923758,0.5307790911639179,0,0.0,False,0.6688662365669343,0.7228582241391484 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+01597500,7,45.36443322753906,6.480633318219866,276.0048,02,EastHghlnds,Non-ref,MD,39.50275,-79.123972,5.276425838470459,23.439816481383726,0.8641320911356439,701.8065547685593,1115.0215964610134,0.5980559546308031,8.101398857196129,1.986417211831577,39.04044825303195,960.9369849748439,0.09775852435496457,0.024863167809959934,0.1362098212353861,0.11369607257683524,0.5555268255498501,8.151577475408756,-15.050000190734863,0.016208748280122344,0.0,0.0,2.478245925501295,33.71344409179687,0.015920931473374367,0.390201899210612,1,0.1636704766098458,True,0.3618425923021301,0.6581349465701611 +01598500,17,175.2026538696289,10.306038462919346,1049.92,02,EastHghlnds,Non-ref,MD,39.479194,-79.065,22.597412109375,23.756545695405556,0.8565109166089874,778.0846603007675,1148.986851575645,0.5950436237973991,7.711319429600564,2.2213545387882294,38.609170795892744,975.2010921265882,0.1779693933962358,0.038828889201510834,0.1313387780072896,0.12383027871737877,0.5499746067963589,8.191055188656518,-15.050000190734863,0.009816711963370158,0.0,0.0,3.023878904367397,82.25728540039063,0.003945628646761179,0.9520519143563729,3,0.2988541634553788,True,0.4126882287128264,0.7013178310256603 +01604500,9,105.55488623046875,11.728320692274306,570.8079,02,EastHghlnds,Non-ref,WV,39.443149,-78.821965,5.789401054382324,24.267232850981223,1.181814711844557,407.6347363579368,948.4639229224326,0.6015573265354865,8.70852713366991,2.197384435873405,38.500938040058145,1117.9504839816937,0.22166021594949048,0.03274080599762591,0.08705701669324849,0.13154460551633368,0.5475674806782048,10.371512413405762,-15.050000190734863,0.0023831811639370122,0.0,0.0,2.7895419562686987,72.8795341796875,0.0010955460602417588,0.8435131270797165,0,0.0041081379638745,False,0.7333941490103133,0.5904144838421295 +01605500,9,78.21512097167968,8.690568996853298,463.8492,02,EastHghlnds,Ref,WV,38.635672,-79.33782,5.814149856567383,23.65415803766401,0.99946595380029,836.0458677114286,1037.398456358868,0.6175217404950643,12.958293185047408,2.010186541935858,39.77566643697533,1031.7820223764265,0.23054402747220082,0.023377333324847455,0.11280399139350722,0.11898708566214763,0.5215539596051965,9.195512773789323,-15.050000190734862,3.0904804270729715e-07,0.0,0.0,2.675773761758319,40.37852136230469,0.004106173757463694,0.46734399724889686,0,0.0,False,0.6862658889399642,0.7100182217578878 +01607500,5,56.607328125,11.321465625,267.1758,02,EastHghlnds,Non-ref,WV,38.631507,-79.24365,3.4754750728607178,22.227224619161948,1.0900170384384884,764.9154419497899,1004.1398454150097,0.6620372418010314,11.710149705660687,2.2174861738313583,42.30347949930897,1083.2310314348765,0.11397461599561438,0.03258993354448167,0.0819414453312245,0.14468967488916865,0.5175601391875435,9.652692530547759,-15.050000190734863,0.0010856854208320724,0.0,0.0,2.598621754639295,47.8419375,0.003136430401355028,0.5537261284722222,0,0.0060873834655345,False,0.3350736699356546,0.4033799926212766 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+02301990,5,42.71941748046875,8.54388349609375,238.9185,03,SECstPlain,Ref,FL,28.185567,-82.183974,2.3664743900299072,16.046830237672683,1.1637991146161806,35.475860144875945,1336.9030290343642,0.6092420185874514,1.8791627395295922,1.8222097093491971,60.28476089205925,1555.6160264236707,0.039557870741286645,0.0,0.00012144450571776732,0.47554228747928046,0.27013444554517774,22.0821591169285,-11.789999961853027,0.008821534050898036,0.0,0.0,2.2397685693708818,31.910372314453127,0.0003577294701244682,0.3693330128987631,0,0.0,False,0.4082106502868003,0.0823403761130898 +02302500,3,36.641734130859376,12.213911376953126,242.9253,03,SECstPlain,Non-ref,FL,28.14057,-82.149807,2.447342157363892,14.863944801706186,1.1710870775781763,39.91321137732813,1324.6276087212902,0.6172548109109418,1.4890241388478653,1.956166065368014,62.42289440096433,1551.1448890118895,0.10628800650717546,0.0,0.00010742799099058958,0.4882447732724468,0.2670760068452133,22.18728942417381,-11.78999996185303,0.006554271568464745,0.0,0.0,2.2582369846949377,34.8028291015625,0.0006097785662859678,0.4028105220088252,0,0.0,False,0.6208327333916313,0.418846863589742 +02303330,25,212.89457468414307,8.515782987365723,985.014,03,SECstPlain,Non-ref,FL,28.097516,-82.312312,8.286590576171875,15.051686930790071,1.1612449318565286,34.593168100196614,1335.4640916921041,0.6203015319278482,1.5268032645555443,1.9155718049562924,62.031525684865855,1550.654693810366,0.117232120543537,0.0,0.00011190865444087707,0.48773990431595177,0.26819572624235916,22.16516005058636,-11.789999961853026,0.006490255598015037,0.0,0.0,2.9626528847964506,55.773279029846194,0.0004540863446891308,0.6455240628454421,0,0.0219784760967003,False,0.7410523828227245,0.8637396785722051 +02303800,9,95.70669897460938,10.634077663845488,407.8359,03,SECstPlain,Non-ref,FL,28.089183,-82.408982,2.3178787231445312,14.123249009779727,1.140100965279846,29.163079530648083,1356.0980090075648,0.6043885062970624,1.4353654494012573,2.0704463786492613,63.67254504220924,1545.8245379202956,0.15604999189473287,0.0,0.00012743644334238936,0.486090879279117,0.26701934180412906,22.0450166732369,-11.789999961853027,0.005235066736254711,0.0,0.0,2.647803325421736,68.42529833984375,0.0003277638752479106,0.7919594715259693,0,0.0,False,0.8013134696701822,0.8597140607953284 +02312720,97,971.7547312927246,10.018090013327058,3981.51,03,SECstPlain,Non-ref,FL,28.82332,-82.183145,17.51003074645996,20.219719536392613,1.2004364466738393,35.06735385675618,1283.5904014800606,0.6384069726906589,1.8079325495466607,2.0689438457109603,54.118210308102206,1539.9659961384903,0.14526884667552445,0.0,0.00015196566398554433,0.4580952884705813,0.27201978196136284,21.6893847443101,-11.789999961853027,0.01251257991480987,0.0,0.0,3.5980384089930166,238.99497344970706,0.0,2.7661455260382763,0,0.0314267956342494,True,0.5704927015659016,0.5493414169300489 +02270500,19,193.8580904388428,10.20305739151804,982.2276,03,SECstPlain,Non-ref,FL,27.442538,-81.297293,8.64690113067627,18.81707327348307,1.2465213942609754,29.329870731942123,1269.8550271709867,0.6002151888497308,1.1688036814581837,2.1378123340195887,56.58973168492602,1582.3623492043253,0.16544683539785174,0.0,6.813055532107028e-05,0.5040813250953443,0.2583456803680173,22.39675594934257,-11.789999961853026,0.1024593551423564,0.0,0.0,3.0035102660997435,88.7421467285156,0.0003926839854102582,1.02710817972819,0,0.0,False,0.7393399474563513,0.8518355476729795 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+04234000,5,68.88793176269532,13.777586352539064,320.6385,04,NorthEast,Non-ref,NY,42.453406,-76.472717,5.671531677246094,21.803144623087547,0.8224392841280087,412.3965431049619,1066.3770523384053,0.585725784949091,4.432740609892385,2.1135119306104717,40.544261134475846,875.0838310770763,0.06910027892235128,0.13223222415350125,0.18444990862209515,0.16197055028823482,0.616964723603434,7.299591544459699,-15.04999409272949,0.006765745487046958,0.0,0.0,2.5188549525992907,49.711328125,0.009720836766064167,0.5753625940393519,0,0.0301918142664894,True,0.8466950520803552,0.8925734044219827 +04239000,3,40.76189013671875,13.587296712239583,245.6235,04,NorthEast,Non-ref,NY,42.983399,-76.150758,3.65445327758789,21.97737315319596,0.8853726419782254,186.6336263108209,1037.8200017871786,0.5280640788603824,4.777759185737133,2.147620153529211,37.575449238124826,918.2767127657412,0.16656216870302604,0.09103246192468867,0.1789207018908317,0.12495909797756083,0.6209231720105589,7.9454947054298986,-12.697608701615264,0.023798508176296876,0.0,0.0,2.4563662661083363,33.2675419921875,0.00116853101644665,0.3850409952799479,1,0.4710104984780751,True,0.6579833317293168,0.6658070413134846 +04240010,3,40.76189013671875,13.587296712239583,331.6887,04,NorthEast,Non-ref,NY,43.057567,-76.162425,4.678554058074951,21.97737315319596,0.8853726419782254,186.6336263108209,1037.8200017871786,0.5280640788603824,4.777759185737133,2.147620153529211,37.575449238124826,918.2767127657412,0.16656216870302604,0.09103246192468867,0.1789207018908317,0.12495909797756083,0.6209231720105589,7.9454947054298986,-12.697608701615264,0.023798508176296876,0.0,0.0,2.4563662661083363,33.2675419921875,0.00116853101644665,0.3850409952799479,1,0.3660817668827337,True,0.7625295872118569,0.8377629865641647 +04240180,3,19.496974700927733,6.498991566975911,116.928,04,NorthEast,Non-ref,NY,42.9209,-76.329376,1.192012906074524,22.274439369354127,0.8320839125724103,277.17090118809,1073.9833494041607,0.5658788537537918,4.710809878043664,1.8585167165825525,36.9013867139204,893.2530843153784,0.14415576785957382,0.2224354055655786,0.18393900638924157,0.12739031540933196,0.6189711958497539,7.5445454668452,-13.362001054038911,0.11230300702727054,0.0,0.0,2.131003222976305,19.063560546875,0.0027052671648561954,0.2206430618851273,1,0.776111513000659,True,0.5235426439798181,0.6400127025628777 +04240300,7,48.79666995239258,6.970952850341797,289.476,04,NorthEast,Non-ref,NY,43.0809,-76.226316,4.130971908569336,22.20499749387584,0.8589180242586802,237.20747166859806,1059.0223781830518,0.5526343123181673,4.159876975372272,1.987842509036039,37.05161031978153,908.9107846057088,0.12023435981660172,0.1555654726272467,0.17808141312285325,0.12060686174488787,0.619573712367996,7.8855500769115405,-12.506045072364103,0.06249957171466601,0.0,0.0,2.4732361193321504,45.786975341796875,0.0005107135511934757,0.5299418442337601,1,0.2210333478504365,False,0.6958698946288131,0.774106412040783 +01516350,9,58.53067797851563,6.503408664279514,395.1621,02,NorthEast,Non-ref,PA,41.794241,-77.080246,6.619900226593018,21.41721991679011,0.9489974202641245,562.8188648661123,977.4313590136516,0.5812595826405855,6.745992726385332,2.0504317014614686,42.61754714838884,923.2611222384523,0.10413976473693232,0.09720771519787545,0.16295710908128586,0.16476770242635957,0.6021524204958488,7.0110394344704305,-15.050000190734863,0.0027450543341289942,0.0,0.0,2.618571029268169,38.713375000000006,0.002261189743876457,0.4480714699074075,0,0.0,False,0.6578396583806162,0.6356998853854056 +01518000,15,118.02792944335935,7.868528629557291,724.5027,02,NorthEast,Non-ref,PA,41.908407,-77.129415,14.076419830322266,21.344034728533348,0.9717713746763257,558.6734700825908,971.0811565565251,0.5846406805248225,7.536324460844776,2.0687940523213926,42.41608155517387,939.5729752441667,0.10130248637222682,0.09653510782303648,0.16274430326229733,0.16608021890941363,0.6027481509553112,7.074467024539389,-15.050000190734862,0.0038747988331267547,0.0,0.0,2.8678935913552204,55.100572753906256,0.0031683812849223614,0.6377381105776188,1,0.0292994853406407,True,0.5263388544680934,0.7312064025729146 +01518700,27,185.1163348388672,6.856160549587673,1142.424,02,NorthEast,Non-ref,PA,41.952574,-77.115249,15.983783721923828,21.261162526092438,1.0100007366302652,551.375305364782,949.8831750695546,0.5877765884302685,7.830951092731141,2.1049367295577674,42.47561691968332,955.3270256544607,0.12744514000637844,0.08063759951493453,0.16091968282744257,0.1712302030388884,0.6043732447494506,7.141622011026844,-15.050000190734865,0.005505938741134624,0.0,0.0,3.0777231082866576,67.27416357421876,0.0009198901825584471,0.7786361524793839,2,0.0467857685578575,False,0.5019327731689471,0.6622989095887597 +01518862,5,36.88079345703125,7.376158691406251,233.9109,02,NorthEast,Ref,PA,41.923126,-77.53193,3.499808549880981,21.27911432894041,0.9597808431217136,564.0207379291097,966.5035612219225,0.5911838368640537,8.216755567118463,2.036504612134326,41.90666845163107,926.941372331175,0.06749444937393724,0.048223539093404946,0.18204819902725713,0.17055426432588305,0.6135734463152589,6.756822355316153,-15.050000190734862,0.0012536722320537273,0.0,0.0,2.4417675137861483,22.2826162109375,0.0035999284591525793,0.2579006505895544,0,0.0,False,0.764804106760641,0.7615301795756884 +01520000,15,114.3217041015625,7.621446940104167,776.2662,02,NorthEast,Non-ref,NY,41.99674,-77.139972,9.851058959960938,21.125616986525177,1.0288710078339216,527.1379932480281,928.4592231613342,0.5853754973600325,8.043118733079545,2.171578583801561,42.309179777082825,952.4036032690267,0.0676053682784937,0.04436811902305667,0.17168005789020344,0.18262289838406934,0.6117764791952508,6.911557387260332,-15.05000019073486,0.0023331434303653508,0.0,0.0,2.8919934435751333,63.75745556640625,0.0024244743399322033,0.7379335135000723,1,0.1237886827389487,True,0.4265972186554428,0.6979526135360717 +01532000,9,96.82190161132812,10.757989067925347,553.941,02,NorthEast,Ref,PA,41.70702,-76.484665,9.006375312805176,21.60429780708967,0.9436767788387639,512.8192342975406,985.3146310619519,0.5862364998844161,6.646448280563726,2.1087063911733575,41.73377496840403,926.6423821960574,0.09195875654868994,0.12189824030813962,0.16286579211514127,0.14459906904990108,0.5975017968757766,7.437677272388803,-15.050000190734865,0.0014064821533824367,0.0,0.0,2.750527587346389,45.216622070312496,0.004444836173206568,0.5233405332212094,0,0.0,False,0.6773414014331021,0.7700264249527988 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+01563500,127,1014.3425946350096,7.986949564055195,5261.855,02,EastHghlnds,Non-ref,PA,40.392299,-77.935,85.06568908691406,23.380534698913802,0.9845925871779331,440.2920815748191,1022.3787912208171,0.5907201165957005,7.784590572087339,2.1224830072703575,37.86531745332899,1003.10190738769,0.1539302575872345,0.035211984080517976,0.11907465161443921,0.11642672218939397,0.5737127527372663,9.30741281611882,-15.050000190734863,0.007030359960987531,0.0,0.0,3.727161822867526,223.21070474243166,0.0008024565759114921,2.5834572308151813,1,0.2696125801097618,False,0.7887062973020363,0.8560359405197394 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+01188090,19,173.66511529541015,9.14026922607422,977.783,01,NorthEast,Non-ref,CT,41.755933,-72.886768,21.264408111572266,8.823141675545632,0.7204821742193973,316.4903490020504,1303.0140978771376,0.6337724776570313,6.53249934927686,2.1429966912734857,60.067205190866375,938.261468951101,0.12321810204502325,0.2519902723752743,0.2039154042475672,0.07667130295118453,0.5971955968317225,7.681218724579352,-15.050000190734863,0.03086690898014689,0.0,0.0,3.0068936325453826,78.38623046875,0.0025771199725568295,0.9072480378327547,8,0.444693634587043,True,0.6542187645464407,0.7569811788625244 +01189995,29,287.40940802001955,9.91066924206964,1492.974,01,NorthEast,Non-ref,CT,41.908431,-72.760651,35.55806350708008,8.869094603744855,0.7467123968069056,258.46391157440115,1278.7448374685468,0.6267944751874851,5.898409170358333,2.190974004620633,60.091699746287496,952.726249367957,0.11926487188023159,0.21484770027092984,0.18977664166594901,0.07787410407981128,0.5934462423847469,8.174924264858976,-15.050000190734863,0.023023709837445453,0.0,0.0,3.190039026793907,134.0154521484375,0.001475475961342454,1.5511047702365452,8,0.2792687244477018,True,0.6276271353725336,0.7780319477713353 +01192500,5,46.00832470703125,9.20166494140625,190.6678,01,NorthEast,Non-ref,CT,41.783155,-72.587311,3.872948408126831,8.926090348990584,0.8217364596962208,138.1573913917052,1187.6438005196082,0.582177980687789,3.3624083314600828,1.8808006348542003,60.584960582756246,974.8964452252965,0.0949532932362047,0.09672798752406948,0.15034401715695841,0.08064615977516862,0.5735663733079298,9.003570838812875,-14.655538909469673,0.01636184836258674,0.0,0.0,2.3152425275129964,40.00739453125,0.001351347891613841,0.4630485478153935,1,0.0989068611159534,True,0.787581704244686,0.8433108970394108 +01193500,9,46.3083984375,5.145377604166667,271.2563,01,NorthEast,Ref,CT,41.552321,-72.449253,5.429888725280762,9.553894898257054,0.7782732508215864,137.52363350712736,1261.213105570779,0.6172546512832935,4.316338177663929,1.8211619919298685,59.24874899782752,981.2464085083171,0.09215278261135401,0.06861169596903878,0.14895731857103697,0.0738435886489395,0.5665992008573734,9.18508738079936,-14.566249714255948,0.019995932006085283,0.0,0.0,2.474116990335276,26.038688232421876,0.0032501087989658117,0.3013737063937717,0,0.0009339870131968,True,0.7088239018165208,0.7366701344871158 +01196500,7,58.43266552734375,8.347523646763394,285.7488,01,NorthEast,Non-ref,CT,41.449542,-72.840934,7.044678688049316,9.36213831870239,0.7785486382447534,122.83352716124314,1281.5370900970363,0.5871777158949324,3.735713018145378,2.027098425928958,59.80806670430163,997.6272104960418,0.12939646643859704,0.0903139322476487,0.1275712745774865,0.06912185677160293,0.570079895585731,9.711525812653168,-15.050000190734863,0.007630612067976663,0.0,0.0,2.535695214157311,46.513865478515626,0.0009604304796084762,0.5383549245198568,0,0.0313250035638808,True,0.5564194565590896,0.52113165748079 +01197000,3,31.02867626953125,10.34289208984375,149.5539,01,NorthEast,Non-ref,MA,42.469529,-73.196492,3.405682325363159,10.273529875438689,0.6936734125066024,534.2900170532472,1289.2164062189004,0.5668185213075552,5.0830165933563185,1.9610313132643438,56.71866785382159,889.2696548591073,0.14373166926636227,0.25862327551195025,0.23328898802394277,0.10213031078661025,0.612567736244019,6.418670568599941,-15.050000190734865,0.016232693751419017,0.0,0.0,2.2719274696883835,24.99487109375,0.0037957532331347466,0.28929248951099534,0,0.0131266572738123,True,0.6520418311836884,0.6418113224260273 +01197500,17,123.71255603027345,7.277209178251378,732.8628,01,NorthEast,Non-ref,MA,42.232033,-73.354833,16.957595825195312,13.579563557862908,0.7301815539459859,463.6934075672269,1247.1573374980944,0.5793702028487027,6.622435147348904,2.0715613461772726,51.59906549146327,906.0298792705481,0.08656328510771305,0.25268860728855397,0.2253343522480224,0.11164363507539064,0.6098300071946816,6.702999596739643,-15.050000190734865,0.021110894686954262,0.0,0.0,2.8679657134593244,72.69657983398437,0.003010723041370511,0.8413955999303747,1,0.0432924616201887,True,0.8197701683649021,0.8955954415059985 +01199000,43,323.74546228027344,7.528964239076126,1643.036,01,NorthEast,Non-ref,CT,41.957316,-73.369286,34.85031509399414,14.002539960139913,0.7573765020438323,415.86020257143207,1225.0811737749598,0.6053136484063053,6.850020152558314,2.01069191991931,51.08985459544177,922.5049751809167,0.11458213909514654,0.22828678038811448,0.2149245986128336,0.11088459803416542,0.6046426246984549,7.0453833432487185,-15.050000190734863,0.015740321752982997,0.0,0.0,3.218206229460342,119.48104931640624,0.0059910304844379425,1.3828825152361832,1,0.0235299693381927,True,0.7396282375838301,0.8499639678187312 +01199050,3,11.777680374145508,3.925893458048503,76.0986,01,NorthEast,Non-ref,CT,41.942316,-73.390953,1.5880142450332642,17.51004986688253,0.835150572457196,354.91138464302094,1165.6909238926537,0.6148371290591688,7.262993201934352,1.6408103021696323,46.24970360489235,972.7703335827198,0.21168211535617723,0.1363117056604336,0.2046262966708563,0.11914024681555387,0.5952758341961235,7.6027385980690125,-15.050000190734863,0.032369662968495506,0.0,0.0,1.9561908453681607,11.52444140625,0.003513679374009371,0.13338473849826388,0,0.0,False,0.7205861944378198,0.7229034709005471 +01200500,65,499.7361314239502,7.68824817575308,2583.356,01,NorthEast,Non-ref,CT,41.65315,-73.489846,54.71757507324219,15.75761524312106,0.780085948975746,361.8925133412642,1211.884374424167,0.605444148613689,6.964978901557803,2.1260234633569923,48.730964908467456,940.8195395428681,0.12272554128352123,0.17772091430506656,0.19919463341641436,0.11080453132438338,0.5976379673961849,7.47427363493156,-15.050000190734863,0.014917029217270705,0.0,0.0,3.425914671887098,174.37518701171874,0.001334783504717052,2.018231331154152,1,0.015310067031772,False,0.8034799940106689,0.8680198597953991 +01204000,5,41.32135400390625,8.26427080078125,195.2595,01,NorthEast,Non-ref,CT,41.480651,-73.224559,4.258468627929688,9.261512169278278,0.7642830077669891,221.80142383798037,1279.4238202961108,0.6195369831996432,6.0693384846711895,1.9573273166006329,59.86411961352832,977.2731936652691,0.048827588762719476,0.02875151786713116,0.15082931002738095,0.08825771415556583,0.5743308275991892,8.978114130249928,-15.050000190734863,0.027540428704071914,0.0,0.0,2.3633050333486216,31.093021484375,0.0019822658505290747,0.35987293384693286,0,0.0,True,0.6802849524139856,0.6600954270974073 +01205500,99,764.8729337921143,7.725989230223377,3998.594,01,NorthEast,Non-ref,CT,41.383985,-73.167612,88.83600616455078,13.888713175145918,0.7747341633607229,314.3509104879782,1232.9266926875744,0.6077095222051225,6.77876733402576,2.124487083018475,51.999343540414536,951.702107231561,0.1250449485177618,0.13341651103047278,0.18309342807066606,0.10189217914250183,0.5904191051080759,7.946392718582007,-15.050000190734865,0.02253276855079097,0.0,0.0,3.602838423181531,218.18495471191403,0.00586433382704854,2.5252888276841903,5,0.1373485547106716,True,0.7997763469148402,0.8872282950101871 +01206900,5,46.50853930664063,9.301707861328126,260.7732,01,NorthEast,Non-ref,CT,41.673708,-73.069553,6.397060394287109,8.81484449687861,0.7196570137514875,301.3661712779104,1324.9335765065064,0.6322099138506951,5.232175746950953,1.9910385391395868,59.884944775659235,953.2583831425412,0.0807268227743823,0.13701706604798905,0.1868644950098688,0.0738383186055511,0.5904828301284687,8.12766108957104,-15.050000190734863,0.016277394652857838,0.0,0.0,2.449524064124378,36.445024902343754,0.0026271063834428787,0.4218174178512008,2,0.5519323640142991,True,0.5336811545281733,0.6677717040170597 +01208500,17,118.26266162109376,6.9566271541819855,674.4304,01,NorthEast,Non-ref,CT,41.442319,-73.062608,16.804380416870117,8.836801898322369,0.7455799396896522,234.1351600273893,1306.1062234324788,0.5910555944924083,5.243763895785865,2.019928043752689,60.643185271624596,973.2558592473058,0.07690775317826178,0.08257911672152263,0.16239319407521843,0.07356295838536654,0.581584962470495,8.755682721256665,-15.050000190734863,0.015702255729197066,0.0,0.0,2.8464107303470945,70.99170947265625,0.002543058479204774,0.8216633040816695,2,0.2154521490781794,False,0.7699265406994593,0.8007517266961305 +01331500,3,26.25106140136719,8.750353800455729,121.0464,02,NorthEast,Non-ref,MA,42.611194,-73.123992,2.7009499073028564,11.21317957791757,0.683291710506466,584.6233984155797,1293.7629075465659,0.5789919724881096,8.023057518380691,2.0045384469213925,54.046061207858955,879.7870998188581,0.07890185819665889,0.19075246798482137,0.23409171351678154,0.11801651896170949,0.6154294114053515,6.362202240261822,-15.050000190734865,0.014331066215866349,0.0,0.0,2.2789797728660974,24.6595625,0.004094601608812809,0.2854116030092593,0,0.0,False,-0.252971508245569,0.0427043866740464 +01332500,5,48.28669714355469,9.657339428710936,328.3083,02,NorthEast,Non-ref,MA,42.700359,-73.158994,8.29218864440918,11.52802881009909,0.6569948248740693,613.5866143809124,1334.4604775750815,0.5746622879755522,8.246225028281899,2.0809793029248778,53.13994263946206,872.1676074303788,0.06086210559734869,0.23369630592682505,0.2396338508640619,0.11726485007128509,0.6170367136848492,6.1758699115983875,-15.050000190734865,0.007963282878616076,0.0,0.0,2.534166132104396,33.05487109375,0.003616478992626071,0.3825795265480324,0,0.0002752351428387,False,0.8284732033116606,0.8795738703179932 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+01103500,13,120.11498120117189,9.239613938551685,473.3199,01,NorthEast,Non-ref,MA,42.256209,-71.260056,9.34911060333252,6.164101771546866,0.798320599197647,70.15599236694896,1247.8165299586844,0.6024788886110204,2.671542295741355,2.1204641700884923,67.98678787070983,995.9670736948872,0.10732144118102442,0.12596485515906805,0.15572618247885348,0.07518477676038261,0.5721610387603383,9.292957936880974,-14.587743079074276,0.01557284757237834,0.0,0.0,2.7652697566196034,87.78601684570313,0.0006269090808928013,1.0160418616400824,0,0.0051239564457606,True,0.8066555074004048,0.866212055660375 +01104200,13,120.11498120117189,9.239613938551685,548.3745,01,NorthEast,Non-ref,MA,42.316486,-71.227833,9.409302711486816,6.164101771546866,0.798320599197647,70.15599236694896,1247.8165299586844,0.6024788886110204,2.671542295741355,2.1204641700884923,67.98678787070983,995.9670736948872,0.10732144118102442,0.12596485515906805,0.15572618247885348,0.07518477676038261,0.5721610387603383,9.292957936880974,-14.587743079074276,0.01557284757237834,0.0,0.0,2.7652697566196034,87.78601684570313,0.0006269090808928013,1.0160418616400824,0,0.0053537561461305,True,0.6935554322102131,0.6935162281387097 +01109060,5,55.48624154663086,11.097248309326172,220.1049,01,NorthEast,Non-ref,MA,41.866212,-71.122824,5.059289932250977,6.883299830790623,0.7818788102109875,53.43236029021219,1263.6842509299347,0.609194749508712,1.0693877292497012,1.8915235462642694,66.0904934830877,988.0220975230043,0.09857417383796782,0.1574463044903753,0.14004463799686276,0.08531522470667269,0.5654000471744343,9.561080772929946,-14.055864753785906,0.004839153285325297,0.0,0.0,2.339477433016285,38.2175810546875,0.0007784361951053143,0.4423331140588831,0,0.0331929280388023,True,0.9036515181658026,0.8854077648542109 +01109403,3,30.329256103515625,10.109752034505208,137.592,01,NorthEast,Non-ref,RI,41.830934,-71.351165,3.290735483169556,7.6540887236434685,0.7729577410208913,48.26804422391294,1259.3590868567117,0.543941580260349,1.0081761832347684,1.8514182736016087,63.774664305092614,973.4783693152986,0.0099999997764825,0.12538317892418072,0.13567290730244372,0.08405348334793486,0.5617479460456906,9.614645185897242,-14.054902850157585,0.012002917089030509,0.0,0.0,2.1461275062301297,27.3255888671875,0.0011944114230573177,0.316268389666522,0,0.0258605859211033,True,0.8856304748161113,0.904896218757856 +01111500,7,37.61510858154297,5.373586940220425,237.2391,01,NorthEast,Non-ref,RI,41.996487,-71.562563,5.244394302368164,8.330510861288923,0.7556783963412816,149.0805129030205,1270.064054880385,0.6198868546991486,2.942630215100597,1.8184369694797715,61.817423500212385,959.49403899417,0.11494611875236374,0.135679441854458,0.16548451262622463,0.07633439346725197,0.569684054897684,8.84215487553453,-15.043342557005843,0.034149847080236315,0.0,0.0,2.3748324083153065,25.55912841796875,0.0027194004505872726,0.295823245578342,0,0.025327036889788,True,0.884575493108368,0.9195196551276336 +01116000,3,30.98990380859375,10.329967936197916,168.2577,01,NorthEast,Non-ref,RI,41.6901,-71.565896,4.037561893463135,7.402846457741925,0.7414262920744014,131.55465413121536,1245.4835112492606,0.6326953165043586,1.9256072739452936,1.9582544531075265,65.06558168068987,923.3924531413444,0.0817653335787067,0.11006080364437482,0.14265229458635972,0.0854857040088214,0.5565882031929962,9.31372334853918,-14.746022454068827,0.017258031223977108,0.0,0.0,2.2739609064901782,26.6698349609375,0.003394740866497159,0.3086786453812211,0,0.0501380900712351,True,0.7678085641052612,0.7994804287818902 +01116500,11,86.33724963378906,7.848840875799005,525.0195,01,NorthEast,Non-ref,RI,41.750934,-71.445058,11.458880424499512,7.840409225868066,0.7394751534790899,128.03294489700818,1259.9521387496538,0.612650695007895,2.0806267694136973,1.976328675516539,63.65485939049977,931.4657983594377,0.13351152168847483,0.13734293728369357,0.1476505747192546,0.08373163436422426,0.5601101246995999,9.231032057709685,-14.72097003066492,0.023145306211261298,0.0,0.0,2.718785313587058,41.59180102539062,0.00010433574789203703,0.4813865859420211,1,0.4348265375289169,True,0.850763472694752,0.8722734088781421 +01117500,5,49.94952099609375,9.989904199218753,260.4375,01,NorthEast,Non-ref,RI,41.4451,-71.680898,5.932470798492432,8.24313946659254,0.7234135791055528,59.78931964500299,1271.49839242332,0.640226423382153,2.483994664870851,2.035398505363987,63.53613066983981,919.4846640076531,0.2006965260218783,0.1347849441842569,0.12586652914584232,0.09354415517007603,0.5402042025533202,9.680830181872269,-14.277491365107975,0.025623680841939646,0.0,0.0,2.4627946106895,33.80749169921875,0.0006187640829011798,0.3912904131854022,0,0.006032064433215,True,0.7503285677139078,0.7030709303534499 +01117800,3,22.77347241210937,7.591157470703124,90.76591,01,NorthEast,Ref,RI,41.573988,-71.720623,2.2141001224517822,7.557825678369408,0.7589512898510036,128.02362951178722,1240.772957448848,0.6433817134489528,3.1115875418452075,1.8842267531255315,64.6038902161288,941.584567405764,0.06789954172521434,0.1465318562627128,0.14093116399600705,0.08534236155214883,0.5533401632720047,9.273278734994449,-14.618158984209767,0.011189951127746019,0.0,0.0,2.195337732314308,21.5465458984375,0.001225241576321423,0.24938131826895255,0,0.0116918244007651,True,-0.4100676244371695,-0.0923917163171432 +01118000,5,37.45523828125,7.491047656249999,193.2111,01,NorthEast,Non-ref,RI,41.498155,-71.716456,4.6718831062316895,7.659911844487992,0.7599692455171907,116.5752464753819,1237.3382679801155,0.6415798995879998,3.0758249739176033,1.9162309088177918,64.43820562459165,940.2422646330714,0.07882910432717895,0.13857825548131952,0.1376690454596791,0.08588760609747208,0.5507159197604997,9.39243784182469,-14.496199528251022,0.012820629623368338,0.0,0.0,2.364036769885765,32.4084306640625,0.0009760137763805687,0.375097577130353,0,0.0129951549170689,True,0.8296385693852684,0.7314116666130104 +01118500,19,143.9050482559204,7.5739499082063375,765.774,01,NorthEast,Non-ref,RI,41.383711,-71.833125,17.18024253845215,8.184558606174805,0.7515729361017615,75.86063533195363,1234.9465332396856,0.634165305089148,2.848168735118408,2.011361537771144,63.482202266612596,927.2797726631503,0.12275421641276411,0.11247912075378966,0.12806398547666334,0.08680274797161981,0.543662093233405,9.629976957643903,-14.40921080919619,0.030836305170947664,0.0,0.0,2.890903087704412,69.02195068359376,0.00030051422072574496,0.7988651699490019,0,0.0062172079379681,False,0.8652583395864766,0.9056794064103438 +01119500,7,60.91304858398438,8.70186408342634,314.8461,01,NorthEast,Non-ref,CT,41.750654,-72.265635,6.716196060180664,8.693195420765846,0.7543825101567377,228.55680683418376,1234.5589667630175,0.6364946728980716,4.745695708777753,2.007859383996123,60.49228068769227,930.9788234373241,0.0574078685822402,0.15551896928233805,0.1769311474521785,0.07242238940452783,0.5766283642791722,8.328681726533,-14.09779953299588,0.01553320756077509,0.0,0.0,2.510417431825084,43.15439794921875,0.0019146635895594954,0.4994721984863281,0,0.0307736379515728,True,0.8197089799802784,0.8994838898426138 +01122500,23,207.2290568847656,9.009958994989807,1048.47,01,NorthEast,Non-ref,CT,41.700376,-72.182022,23.13277244567871,8.928917982173077,0.7709672147960591,193.00030171453423,1228.4327399387034,0.6268244660671296,4.37946640274268,2.049825264908791,60.09791877853614,946.6437565239547,0.08136670791931065,0.1226824825285482,0.16960491511787,0.07056035617242705,0.5728977027909712,8.566734686480302,-14.396588619123287,0.016953852310336876,0.0,0.0,3.034310300546963,62.38035217285156,0.0012305687414482236,0.7219948168154116,0,0.0174739163389006,True,0.7595313341296803,0.8779668180104474 +01124000,7,67.66047729492188,9.665782470703125,392.0004,01,NorthEast,Non-ref,CT,42.022319,-71.955629,8.49370288848877,8.598764967495823,0.7490855280144678,234.92593679678487,1250.4842103253275,0.6272352332100382,4.742975190438717,2.161680321808638,60.87311364064282,936.3537367521606,0.020022245844598136,0.18420512455367655,0.18723474654915986,0.059022713913977676,0.5834096181451801,8.067325328520786,-14.732121636214824,0.018518392701966105,0.0,0.0,2.6436861626692045,53.62933154296876,0.0016611856408417225,0.6207098558213977,0,0.0229294437189681,True,0.8321387461876214,0.8484781427673341 +01127000,37,334.8712418823242,9.050574104927682,1838.699,01,NorthEast,Non-ref,CT,41.597876,-71.984242,40.73202896118164,8.60561658041104,0.7696173874522053,169.1834126282663,1246.334212824095,0.6165438448110308,3.877343474613574,2.133055283980762,61.09106268163541,958.7803920112048,0.06049755813896902,0.12030737324482739,0.16842835956279906,0.0680164735580139,0.5724971756416862,8.646324167941247,-14.968558252746051,0.015797867882697943,0.0,0.0,3.2683099927659574,115.84757373046875,0.0002836938656400889,1.340828399658203,1,0.0544660595328223,False,0.8447624941655516,0.9028504920943514 +01127500,9,45.308601989746094,5.034289109971788,230.7345,01,NorthEast,Non-ref,CT,41.558709,-72.121467,5.0076093673706055,9.675704510021921,0.8006069649733182,115.38359440348698,1255.0228869819148,0.6105022852622292,3.649109744898891,1.7601444778301074,59.07545794884627,1004.6237485362072,0.17243466139871813,0.07701463704759859,0.14436844360705228,0.07749778945787465,0.5651121619203949,9.26621303773396,-14.939260744574165,0.021165737328819097,0.0,0.0,2.40563732151995,27.556776123046873,0.0034469750244170427,0.3189441680908203,0,0.0425949998163685,True,0.782597531751914,0.8716430799665339 +01172500,5,27.03735285949707,5.407470571899414,142.6932,01,NorthEast,Non-ref,MA,42.42509,-72.024521,3.033172369003296,8.205331600835772,0.7284793680742275,318.4453924891721,1244.5319655783085,0.6408129781286432,3.579691547042528,1.6698255944976628,61.53740645486788,905.5657322810525,0.13031487266406924,0.2658245062613567,0.21870761724823767,0.05278239638848526,0.6000671708108702,7.261843285470453,-15.050000059045127,0.028691423673236932,0.0,0.0,2.14692501680414,18.765794189453125,0.011310277506709099,0.2171966920075593,1,0.8559050412818364,True,0.4637778821609105,0.634379275898225 +01173000,9,40.59750343322754,4.510833714803059,248.9728,01,NorthEast,Non-ref,MA,42.390646,-72.060355,5.3436150550842285,8.176549573422731,0.7535559909019969,304.249153398358,1222.2838258526172,0.6485847625494598,3.6742849906252193,1.721544425913928,61.58993838891156,919.6088656728132,0.1132340776100811,0.26382142686842186,0.21730760269820334,0.05439055571361655,0.5999291401899174,7.2653459726442735,-15.049885717737197,0.02098962426844963,0.0,0.0,2.371829725106015,27.383917236328124,0.002225936623290181,0.3169434865315755,1,0.4941013670790822,True,0.7311196341196583,0.8198052747796897 +01076500,41,270.2526378631592,6.59152775275998,1608.937,01,NorthEast,Non-ref,NH,43.759239,-71.685634,46.73666763305664,9.726901202424099,0.6473173674590629,558.8057697333894,1359.4881673719262,0.6124462245389719,11.06317375655947,2.0385539067233993,57.69219981334754,851.2522991246807,0.14624511258684733,0.37307640384086754,0.2732113813829995,0.12163083051230612,0.6519439912138946,4.549582042212557,-14.49396585946202,0.003062228693471436,0.0,0.0,3.223095343559987,77.768631149292,0.00013433044659905136,0.9000998975612501,0,0.0011329894621599,False,0.8154567467589703,0.90072303197712 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+01144000,33,283.1531376953125,8.580398111979168,1790.244,01,NorthEast,Ref,VT,43.714236,-72.418149,39.89446640014648,12.382062468164158,0.7423944858057842,411.53883773148584,1161.1248925045568,0.5779610154130004,11.433475269792128,2.2126804838768863,52.50044919608996,854.4083197084942,0.1563872855252399,0.3454553410486637,0.2552978265071663,0.13740858525291907,0.6497760411245457,5.216232551773242,-14.207278179460625,0.00042960051970718,0.0,0.0,3.2667340977211072,96.04424560546875,0.000962858262937516,1.1116232130262587,0,4.829799224142588e-05,False,0.8265232835112826,0.9033798420408023 +01151500,15,99.17658862304688,6.611772574869791,576.1521,01,NorthEast,Non-ref,VT,43.602571,-72.354258,14.000970840454102,12.978057546014323,0.7466796986229389,448.57805548874944,1210.4009204173985,0.5828128143246565,11.201402602871918,2.02477262987918,50.865773461328864,892.4737954923853,0.11276887593251574,0.333624747545129,0.2562443243609399,0.10420845555866509,0.6381819376549883,5.380496870138155,-14.715451578229038,0.0020072707743098907,0.0,0.0,2.7599801518652773,63.77271105957031,0.0039001775439828634,0.7381100817079898,0,0.01123533047585,True,0.7281067806958418,0.7940027119342616 +01152500,9,100.38954089355468,11.154393432617187,700.3791,01,NorthEast,Non-ref,NH,43.387573,-72.362034,13.757050514221191,9.985103441539113,0.7953221965789086,383.58179051244093,1141.9490408909278,0.6170366181916704,6.682810775659785,2.200707596739046,58.551972936485576,904.8838523735499,0.15708738299121763,0.36159161364345166,0.2370889993021271,0.1034188308045913,0.6243641325399636,6.181716860616056,-14.873696193269565,0.036171611286992165,0.0,0.0,2.823350102742095,55.317627929687504,0.005229726899415255,0.6402503232602721,1,0.0635243083701368,True,0.8472818840614276,0.918456110599282 +01153550,7,53.51330249023437,7.6447574986049105,289.5507,01,NorthEast,Non-ref,VT,43.191743,-72.485089,6.512985229492188,12.237406220803674,0.7597601619705842,383.7668859445735,1187.9956403846318,0.615268948160729,8.512790574954854,1.9793113276248575,53.653258388850205,900.4162088718844,0.11705903655712405,0.34838151071762957,0.24353798540307692,0.076842542180803,0.6277866755382908,6.0742051518186555,-15.049997533071313,0.001185498624253401,0.0,0.0,2.481566822460331,36.059560546875005,0.0044841468334198,0.41735602484809037,0,0.0,False,0.7613837568761838,0.8428841300179636 +01154500,310,2375.137626411438,7.661734278746573,14220.09,01,NorthEast,Non-ref,NH,43.126189,-72.436755,322.18096923828125,11.189019698589163,0.754760609380235,441.1387105573747,1147.0274970175742,0.5931626310731647,8.452233982677308,2.1966590416760057,54.93979939380139,854.0426587252718,0.12827028534207152,0.35585375350760107,0.2606394180419712,0.15035518277071852,0.6545006795830045,4.821615782091464,-14.883800156598213,0.010206165035029574,0.0,0.0005384374375801125,4.152705184336846,384.2915482406617,0.0007813418051227927,4.44781884537803,10,0.0833737971205421,False,0.8031521144101261,0.900823030075978 +01161000,25,193.21501525878907,7.728600610351562,1089.694,01,NorthEast,Non-ref,NH,42.785916,-72.4862,22.733896255493164,8.010974232744873,0.7586917093283099,328.8065921597516,1204.3252693672366,0.6400157616810151,6.636593475666255,2.0968442073687914,61.00883727418599,910.4528893296034,0.09669808922719224,0.3287205772926713,0.23229503234422497,0.08934375672055812,0.6149364199489854,6.517009591806869,-14.412931001000292,0.009131235103077728,0.0,0.0,3.038287226145196,98.77656799316406,0.008380708284676075,1.1432473147356952,0,0.0096586716913332,True,0.7794539168279911,0.8813964079096812 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+01331095,203,1714.7997086486816,8.44728920516592,9784.651,02,NorthEast,Non-ref,NY,42.935632,-73.651784,205.5246124267578,13.15343658937212,0.7413843522014139,431.8920607875843,1202.217946630226,0.5927857997424969,7.9680505135986675,2.2470735997205677,52.576334261942925,878.5981415337449,0.11985124133017139,0.3269049038009395,0.24518647332135513,0.096078948918432,0.6353852606232644,5.800823180140748,-13.597398630625262,0.027792733284676286,0.0,0.0,3.9882736237866028,252.58168890380855,0.0001096770356525667,2.923399177127414,10,0.2180889472969577,True,0.4847395162245395,0.7015003610368997 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+01334500,27,210.11388977050785,7.781995917426216,1324.343,02,NorthEast,Non-ref,NY,42.938689,-73.377056,31.321521759033203,16.402662655357833,0.7256808793882019,468.6841169005773,1257.4108229693204,0.5935586033482007,8.34903366928618,2.119160738662,46.53614702756435,897.1753633768446,0.11969446185158038,0.17665284402415998,0.2212516813021092,0.1345833986843621,0.6149456410513597,6.582278853545769,-14.598881359192,0.0024642064342499724,0.0,0.0,3.1239783798508727,78.4036806640625,0.001027551363222301,0.9074500076859086,0,0.0015369353922389,False,0.8558421122737296,0.9133695662224328 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+04282500,39,311.2601332702637,7.981029058211889,1632.865,02,NorthEast,Non-ref,VT,44.013115,-73.167896,34.06461334228516,15.743549846072089,0.7295272684627825,417.31530592024467,1263.7842900690403,0.5849000426996274,8.334496755923638,2.1168814751059957,46.68327155466897,896.0565213304383,0.09898510026588006,0.24448489030393386,0.22460687881047528,0.14437776585322487,0.6315409534919617,5.954732187220712,-12.727174624059957,0.008672446896109793,0.0,0.0,3.227801531627879,127.71447726440431,0.0021097753196954727,1.4781768201898646,1,0.0327620127554308,True,0.4171713368940724,0.7133207689910543 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+04296500,3,60.8667109375,20.288903645833333,375.0174,01,NorthEast,Non-ref,VT,44.940324,-72.189268,8.0657320022583,10.192431951212193,0.6689784789908931,458.2639168845142,1189.1092304560843,0.5545777032651313,5.261599375772116,2.279610587263609,56.899199518231214,794.1098487699054,0.0927556905859223,0.3468327222160913,0.2838661386638117,0.17283183623610485,0.6737043754627188,3.9804290367571933,-15.050000190734863,0.07398567833030381,0.0,0.0,2.5695487591236237,52.441419921875,0.0044954740442335606,0.606960878725405,0,0.0577188172805727,True,0.8418682777518852,0.8680313309420622 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+01081000,23,204.5491065368652,8.893439414646314,1216.967,01,NorthEast,Non-ref,NH,43.441744,-71.587575,23.574691772460938,11.090079642386527,0.7896804321835391,251.53671506910942,1157.9093524094762,0.6121914514914875,5.620864268692226,2.1873015308665495,57.39683247036099,912.2641834884239,0.13552388708445046,0.35520872288509564,0.23419567771484823,0.09190301366957622,0.6315977895273603,6.340714513390482,-15.04955970976141,0.15549147379338604,0.0,0.0,3.1007594959589464,90.54418676757811,0.004396896809339523,1.0479651246247466,3,0.3527654169536772,True,0.6864209403842663,0.8265503846274872 +01089100,7,48.47285485839844,6.924693551199776,214.1253,01,NorthEast,Non-ref,NH,43.213692,-71.480348,4.114902019500732,10.938648850021801,0.7953039377984394,179.40577423404096,1147.3690164564,0.5596967652172868,4.266890185726348,1.8482979718080499,56.46846509207661,910.058293310834,0.12433395866824912,0.3329353673456484,0.22405689299985015,0.09120525087781206,0.620517032249912,6.83039365212244,-15.050000190734865,0.009113691453090204,0.0,0.0,2.3902444588326546,39.981187011718745,0.0024436188396066427,0.46274522004304103,0,0.0,False,0.7751804934882833,0.7942973178638062 +01092000,181,1377.730070236206,7.611768343846442,7987.65,01,NorthEast,Non-ref,NH,42.948141,-71.4634,184.70899963378903,10.00171288407649,0.747776201766304,314.75896124145913,1223.48566744014,0.6199250316775022,6.9808824563802006,2.1788009740132215,58.40736199412023,902.2268663622028,0.13832811383182558,0.3545068838654803,0.24164452639233547,0.09238453941557724,0.6281950900551416,6.2083978412038485,-14.599402568210055,0.04039805194038659,0.0,0.0,3.8999880442464714,202.7333955535889,0.0009592328569851816,2.3464513374257976,7,0.0829300526971986,False,0.774515608691247,0.8842130683011556 +01094400,5,30.86738220214844,6.173476440429687,166.1607,01,NorthEast,Non-ref,MA,42.576201,-71.788129,3.678483009338379,7.978568345823907,0.7548669242983942,286.70851522733807,1230.520150380287,0.5934472624693073,4.324202522211061,1.8205358505746314,62.44299244425692,928.4642793785013,0.13552699718580158,0.27437981796131655,0.2257380761612362,0.06181544771655668,0.6039021852590233,7.251432307489981,-14.854243367552245,0.04777150217818933,0.0,0.0,2.2588944762176237,21.74939514160156,0.006838410161435604,0.25172911043520324,0,0.0330498711482634,True,0.8453437215730868,0.901559837251612 +01094500,9,54.64498278808594,6.071664754231771,279.657,01,NorthEast,Non-ref,MA,42.501758,-71.722571,6.866598606109619,8.016757745695006,0.7717646799051905,245.60462966752192,1219.6165102155771,0.5714413857991366,4.4049543568125955,1.8407547884671458,62.529730714515594,940.3709047381683,0.1371104208727818,0.25109508850248047,0.21624979558264662,0.06357967910703932,0.6015124103599496,7.486652069786266,-14.83472247549591,0.038459558946283105,0.0,0.0,2.474789661768829,33.32261828613281,0.0014761571073904634,0.38567845238579634,0,0.0186712906047845,False,0.8584750651090134,0.8417622293471994 +01096000,3,29.626422119140624,9.87547403971354,173.1357,01,NorthEast,Non-ref,MA,42.634256,-71.657848,3.718006610870361,7.5050879388221725,0.8038360085460146,204.7779282288827,1204.4488307408446,0.6549450944185538,4.650604419771659,1.9690720060040963,64.40063277661011,967.2633703946905,0.2371624967490556,0.2906405061923143,0.20740090535760078,0.07249270943266792,0.6036731657198868,7.82604890958255,-14.467349776201873,0.005036640127454957,0.0,0.0,2.2766727313235346,27.14665234375,0.0012505262857303023,0.3141973650896991,0,0.0033034725420824,True,0.8854973017232378,0.9289783740333352 +01096500,27,205.7457705078125,7.620213722511574,1125.681,01,NorthEast,Non-ref,MA,42.667589,-71.575068,19.43488883972168,7.788476389722808,0.7933052855076741,190.7671564837923,1207.4819531407895,0.6014041459435474,4.094893233744076,2.041630121625955,63.45879861146654,956.9757037721091,0.16657301280563303,0.2434399585291799,0.20149397974735714,0.06398909898940558,0.5954481569250581,7.916000586711818,-14.833843191858008,0.03092715630037596,0.0,0.0,3.060240570552927,79.8237353515625,0.0003646518453024328,0.9238858258282697,1,0.408902871616749,True,0.7174534538974917,0.8572430652192881 +01097000,5,59.62278515625,11.92455703125,299.3094,01,NorthEast,Non-ref,MA,42.432038,-71.449785,6.349416732788086,7.555563490670646,0.8074186598287499,105.41159563839214,1211.8647757951,0.6091981874756667,3.420784122180886,2.0286628908225044,64.11469460771386,978.4573621098646,0.0926963127662348,0.17020310583197062,0.17316396406101503,0.06535064606288815,0.5796358737731292,8.641452934710903,-15.049987079378752,0.060142441116120465,0.0,0.0,2.466876085747633,46.6285810546875,0.001478459220379591,0.5396826510959202,0,0.0099543713873173,True,0.8077336947777367,0.6593053991383694 +01099500,19,209.98515991210937,11.051850521689966,1035.51,01,NorthEast,Non-ref,MA,42.636758,-71.302005,21.42818260192871,6.432469959680674,0.8127426438531572,91.09128723045062,1214.1064202113248,0.6041096949613902,2.985526789410077,2.1133709065100024,67.44563117820553,986.626251177253,0.08009040402928849,0.16983635457519528,0.16842369196928372,0.0680057038850016,0.5794925776402059,8.863803321462834,-15.037156832406225,0.032414442175299214,0.0,0.0,3.0174640102440935,81.001861328125,0.004741401877254248,0.9375215431495949,1,0.0549974198650638,False,0.8349572598782098,0.7961323276974455 +01100000,263,2147.775605484009,8.166447169140717,11983.35,01,NorthEast,Non-ref,MA,42.645925,-71.298394,263.8345031738281,8.99795885439682,0.7697714866022266,252.94684522182413,1213.0933740754792,0.6166134357149712,5.839273570158186,2.2027387104708986,60.887559903351274,924.6138361353925,0.1320583809295434,0.31332474624336165,0.22434248461563422,0.08538698327245113,0.6161660163150785,6.904354478249027,-14.677301786542138,0.035168422890967915,0.0,0.0,4.080416969734303,269.321431930542,0.0005539555568248034,3.1171462028997916,10,0.0969983581703569,True,0.8046679607307667,0.8964110196641656 +01101500,3,29.667009765625,9.889003255208332,115.2826,01,NorthEast,Non-ref,MA,42.569539,-71.026997,2.1514289379119877,5.505178432795901,0.7708005952893244,35.52407671796747,1242.2709663666944,0.5967347545719809,2.2657678316558902,1.9299139253344186,70.81370147672902,957.4639779374301,0.08857261556217697,0.1833571510794162,0.16009845450428647,0.07969052014669699,0.5777178467537862,9.281316624627308,-14.297538105832725,0.024082386922860737,0.0,0.0,2.2308941532476436,24.87147900390625,0.0004829191602766514,0.287864340322989,0,0.0006310710171493,True,0.5370671759272259,0.4378728371902809 +01102000,7,60.91511865234375,8.70215980747768,316.4265,01,NorthEast,Non-ref,MA,42.659816,-70.893662,6.239163875579834,5.873347978266779,0.762731113200349,36.614436763259,1243.9942130323886,0.613599008424179,2.3689938581775647,2.018316832969954,69.64125125391438,948.6788496258276,0.08215511514384588,0.20191127407352918,0.16120535039826325,0.08429652457116626,0.5770134782001899,9.274508400290532,-14.21430890219636,0.025289162863498693,0.0,0.0,2.5326272811144683,46.24807397460937,0.00032765415380708873,0.5352786339653862,0,0.0002243227175071,False,0.9040295253891312,0.8840103969632831 +01104500,15,129.14148706054686,8.609432470703124,647.4123,01,NorthEast,Non-ref,MA,42.372319,-71.233667,10.983777046203612,6.174044403613149,0.798654246534248,69.37757671629127,1246.0763016413398,0.598592464416647,2.6830255598792965,2.0965132574097973,68.0112159236464,994.9930301208844,0.09803105866628635,0.12758726205355853,0.15620273146822536,0.07487818620841925,0.5726400004767611,9.290116612337144,-14.590376404774773,0.016315530846836276,0.0,0.0,2.808842363317996,92.43288842773438,0.0006236549816094339,1.0698250975432222,0,0.0172494389309501,True,0.7584427742603722,0.7428242597649017 +01038000,9,97.63299328613282,10.84811036512587,375.0849,01,NorthEast,Non-ref,ME,44.222778,-69.593758,7.774347305297852,8.35289002795716,0.7342068312314379,91.58000351401726,1180.283725734407,0.6434978079075886,3.152125610169891,2.129001232315119,61.468047348499304,866.3024050468542,0.06587752242196941,0.33844836192170297,0.24091547541381636,0.10516179512556283,0.6327522516343035,7.089889226197995,-15.050000190734863,0.04585983194371111,0.0,0.0,2.6773840530794093,62.91939013671876,0.0011425252305343747,0.7282336821379486,1,0.0434383846906051,True,0.6552032168020325,0.6307322730371001 +01047000,17,168.75977209472657,9.927045417336856,909.0972,01,NorthEast,Ref,ME,44.8692,-69.955103,25.3994083404541,10.387874400424641,0.6755961446919636,350.45853728466386,1220.4954905263287,0.5957506575897396,7.6869968726026165,2.103414539440029,57.354702662247725,814.2571437896933,0.14028817829869042,0.4177632510833127,0.29760940113101125,0.12182434623111191,0.6732117495338841,4.260461875151046,-14.51491625223429,0.019587325702413495,0.0,0.0,2.9714674774650427,71.22568017578125,0.001649342244490981,0.8243712983308015,0,0.0028035083500133,False,0.7919981907560134,0.8955118135041821 +01048000,31,236.0700227050781,7.615162022744455,1339.138,01,NorthEast,Non-ref,ME,44.708169,-69.937592,33.0697135925293,10.77346122103642,0.6818634204593435,308.1313508295271,1248.2116277472055,0.583815725706614,6.756616836072091,2.0938158329133163,56.3793645988057,841.2763397414686,0.12035900506384672,0.38725209194756244,0.28719673728117406,0.11513040752299666,0.6632004061549258,4.9486562555257585,-14.896488942730443,0.011267697847040596,0.0,0.0,3.126276768271032,104.82849560546873,0.001936586806550622,1.2132927732114438,1,0.0135875276004622,False,0.8228689667502429,0.8970988682481448 +01049000,31,251.77781021118165,8.121864845521989,1488.607,01,NorthEast,Non-ref,ME,44.716778,-69.413819,29.734304428100582,8.849915120986486,0.7670192159909729,130.25682131434925,1126.9573338447922,0.615484423603976,2.8881701503310357,2.108204674765766,60.895243721954486,863.5259642218615,0.1302654110819326,0.3859829281973082,0.2675702413837916,0.1128580581584456,0.653203848914087,5.650205627826482,-14.971195883739968,0.04952600399552323,0.0,0.0,3.174529083728022,85.39008422851562,0.000653822731692344,0.9883111600522642,3,0.0663315214133134,True,0.8113662339404402,0.8941012917162049 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+01017000,106,798.866670135498,7.536478020146207,4278.907,01,NorthEast,Non-ref,ME,46.777294,-68.157194,82.16680145263672,10.608162419886295,0.7704241981887432,266.44881191284895,1041.9613032450593,0.6258379076307364,3.538294231908378,2.1166135378573387,55.500589409093514,801.5365547074666,0.045505725343845345,0.4793117793546104,0.3015701991910768,0.13903231296387275,0.7079464520283966,3.2691170850401474,-15.050000190734862,0.021359595776270508,0.0,0.0,3.6355079762186713,159.47120404052737,0.0005727351526729763,1.8457315282468445,2,0.0394383655610343,False,0.9117108541182118,0.9240919788804652 diff --git a/experiments/levers/inventory_runs.py b/experiments/levers/inventory_runs.py new file mode 100644 index 00000000..f5d745e4 --- /dev/null +++ b/experiments/levers/inventory_runs.py @@ -0,0 +1,41 @@ +"""List every run in the main workspace with a test-phase predictions.zarr: config essentials and metrics.""" +import json +import pathlib +import re + +import yaml + +RUNS = pathlib.Path("/home/tbindas/projects/ddrs/.ddrs/runs") +EV = "ev" + "al" +rows = [] +for d in sorted(RUNS.iterdir()): + pz = d / EV / "predictions.zarr" + if not pz.exists(): + continue + try: + m = json.load(open(d / "manifest.json")) + except Exception: + continue + try: + c = yaml.safe_load(open(d / "config.yaml")) + except Exception: + c = {} + ds = c.get("data_sources", {}) or {} + ex = c.get("experiment", {}) or {} + te = c.get("testing", {}) or {} + kh = c.get("kan_head", {}) or {} + pr = c.get("params", {}) or {} + met = m.get("metrics", {}) or {} + rows.append(dict( + run=d.name, status=m.get("status"), gages=pathlib.Path(str(ds.get("gages", ""))).name, + streamflow=pathlib.Path(str(ds.get("streamflow", ""))).name, seed=c.get("seed"), + epochs=ex.get("epochs"), loss=(ex.get("loss") or {}).get("kind", "l1"), ckpt=bool(ex.get("checkpoint")), + test=f"{te.get('start_time')}-{te.get('end_time')}", learn=",".join(kh.get("learnable_parameters", []) or []), + res=pr.get("use_reservoirs", False), leak=pr.get("use_leakance", False), + n=met.get("n_gauges_total"), nse=met.get("median_nse_finite"), kge=met.get("median_kge_finite"))) +for r in rows: + if r["gages"] != "gages_3000.csv": + continue + print(f"{r['run'][:40]:40s} {str(r['status'])[:8]:8s} seed={r['seed']} ep={r['epochs']} {r['loss']:9s} ck={int(r['ckpt'])} " + f"{r['test']:22s} learn={r['learn']:28s} res={int(bool(r['res']))} leak={int(bool(r['leak']))} {r['streamflow'][:30]:30s} " + f"n={r['n']} nse={r['nse']} kge={r['kge']}") diff --git a/experiments/levers/launch_replay.sh b/experiments/levers/launch_replay.sh new file mode 100755 index 00000000..1b67c861 --- /dev/null +++ b/experiments/levers/launch_replay.sh @@ -0,0 +1,11 @@ +#!/usr/bin/env bash +# Launch one zero-step training-years replay, fully detached. Usage: launch_replay.sh +set -euo pipefail +SEED="$1" +HERE=/home/tbindas/projects/ddrs/.claude/worktrees/agent-a92e512a7c47c97b4/experiments/levers +BIN=/home/tbindas/.claude/jobs/dacd6d8c/tmp/bin/ddrs-v6-77aa4a0 +cd /home/tbindas/projects/ddrs/.claude/worktrees/agent-a92e512a7c47c97b4 +DDRS_JOURNAL_OFF=1 RAYON_NUM_THREADS=8 setsid nohup "$BIN" --config "$HERE/replay_trainyears_s${SEED}.yaml" \ + --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu \ + > "$HERE/replay_s${SEED}.log" 2>&1 < /dev/null & +echo "pid $!" diff --git a/experiments/levers/loss_curve_2026-09-12T03-53-34Z.csv b/experiments/levers/loss_curve_2026-09-12T03-53-34Z.csv new file mode 100644 index 00000000..2346d4da --- /dev/null +++ b/experiments/levers/loss_curve_2026-09-12T03-53-34Z.csv @@ -0,0 +1,51 @@ +epoch,loss,n_micro,median_n,loss_med +1,0.2769927128964059,37.0,0.11777,0.253614 +2,0.2952869860465116,37.0,0.08096,0.272063 +3,0.27926875560253706,37.0,0.05414,0.183208 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+48,0.27266278097251584,37.0,0.07742,0.225783 +49,0.33100348456659623,37.0,0.05725,0.276827 +50,0.3088610875264271,37.0,0.06722,0.234486 diff --git a/experiments/levers/loss_curve_2026-09-12T23-39-06Z.csv b/experiments/levers/loss_curve_2026-09-12T23-39-06Z.csv new file mode 100644 index 00000000..abda0399 --- /dev/null +++ b/experiments/levers/loss_curve_2026-09-12T23-39-06Z.csv @@ -0,0 +1,51 @@ +epoch,loss,n_micro,median_n,loss_med +1,0.29620029682875265,37.0,0.10302,0.284965 +2,0.298204155179704,37.0,0.0443,0.280796 +3,0.2831263496828753,37.0,0.04282,0.198031 +4,0.2712754575052854,37.0,0.05427,0.2332 +5,0.2749001090909091,37.0,0.05385,0.217021 +6,0.3668921179704017,37.0,0.05607,0.286383 +7,0.31426024566596195,37.0,0.06241,0.222922 +8,0.3015130025369979,37.0,0.06214,0.267917 +9,0.31438918224101475,37.0,0.05751,0.264 +10,0.30467814714587743,37.0,0.05595,0.265514 +11,0.31534037716701896,37.0,0.06059,0.291304 +12,0.2876561450317125,37.0,0.06371,0.231832 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+epoch,loss,n_micro,median_n,loss_med +1,0.2814121619450317,37.0,0.11708,0.260432 +2,0.30049418477801265,37.0,0.08788,0.28207 +3,0.2835163885835095,37.0,0.06697,0.20484 +4,0.27484764312896404,37.0,0.05016,0.234444 +5,0.28013785792811835,37.0,0.0471,0.218634 +6,64785.09696942833,37.0,0.06302,0.31768 +7,0.3171587226215645,37.0,0.07716,0.22488 +8,0.30568749006342494,37.0,0.08685,0.275161 +9,0.30389433276955596,37.0,0.07941,0.264275 +10,0.3085993517970401,37.0,0.09641,0.269577 +11,0.3189015095137421,37.0,0.08489,0.298324 +12,0.29219153657505287,37.0,0.08564,0.238713 +13,0.26437375221987314,37.0,0.06212,0.213826 +14,0.27002231458773784,37.0,0.06566,0.226882 +15,0.3450747082452431,37.0,0.06343,0.32336 +16,0.27615474460887945,37.0,0.08394,0.220898 +17,0.3123080938689218,37.0,0.10625,0.227579 +18,0.31761169767441866,37.0,0.07782,0.26663 +19,0.29252243594080335,37.0,0.078,0.237893 +20,0.34911334841437636,37.0,0.07593,0.254224 +21,0.27697392727272724,37.0,0.08463,0.232059 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+44,0.25891082241014796,37.0,0.05641,0.198508 +45,0.29520389894291754,37.0,0.05944,0.276651 +46,0.31242611035940804,37.0,0.06402,0.273657 +47,0.27293872811839326,37.0,0.07021,0.21305 +48,0.27920694334038054,37.0,0.06488,0.232667 +49,0.33729618308668075,37.0,0.05551,0.283123 +50,0.3171744494714588,37.0,0.05885,0.2429 diff --git a/experiments/levers/loss_curve_2026-09-27T10-31-30Z.csv b/experiments/levers/loss_curve_2026-09-27T10-31-30Z.csv new file mode 100644 index 00000000..539c2ade --- /dev/null +++ b/experiments/levers/loss_curve_2026-09-27T10-31-30Z.csv @@ -0,0 +1,51 @@ +epoch,loss,n_micro,median_n,loss_med +1,0.2614124355179704,37.0,0.11095,0.243219 +2,0.30767857801268506,37.0,0.07327,0.310524 +3,0.3211429251585624,37.0,0.08815,0.324023 +4,0.2623402350951374,37.0,0.07818,0.231765 +5,0.27060125031712473,37.0,0.06068,0.219315 +6,0.28578851966173374,37.0,0.06446,0.263436 +7,0.35253524439746303,37.0,0.06034,0.298055 +8,0.30498414545454544,37.0,0.06008,0.232826 +9,0.27896140465116276,37.0,0.05216,0.241463 +10,0.3002731847780126,37.0,0.04667,0.201167 +11,0.29006934968287523,37.0,0.05697,0.287765 +12,0.3259863154334038,37.0,0.07192,0.272677 +13,0.33665569598308664,37.0,0.05057,0.224905 +14,0.2849990114164905,37.0,0.06017,0.221636 +15,0.30009932854122623,37.0,0.07391,0.2665 +16,0.2938773353065539,37.0,0.09186,0.247873 +17,0.24778698012684994,37.0,0.07529,0.219381 +18,0.308734111205074,37.0,0.07035,0.318221 +19,0.27390066934460894,37.0,0.08072,0.256383 +20,0.255161333192389,37.0,0.06558,0.210185 +21,0.29330748752642705,37.0,0.06727,0.274819 +22,0.33539256701902753,37.0,0.06142,0.239415 +23,0.25910493065539114,37.0,0.06637,0.191708 +24,0.2852847086680761,37.0,0.06108,0.270073 +25,0.28915677082452435,37.0,0.06389,0.272255 +26,0.2529371682875265,37.0,0.07627,0.216637 +27,0.2794318287526428,37.0,0.06695,0.202615 +28,0.3143959403805497,37.0,0.06313,0.264202 +29,0.28092434164904867,37.0,0.06722,0.234733 +30,43792565.23568824,37.0,0.07138,0.212388 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mode 100644 index 00000000..d1d7f405 --- /dev/null +++ b/experiments/levers/loss_robust.py @@ -0,0 +1,25 @@ +"""Robust loss-curve slopes: per-epoch MEDIAN micro-batch loss (seed 43 has one exploding micro-batch in epochs 41-50), +OLS and Theil-Sen slope over epochs 41-50 and 31-40, and the change from epochs 6-10 to 41-50. Writes loss_robust.txt.""" +import glob + +import numpy as np +import pandas as pd +from scipy import stats + +import common as C + +out = [] +for f in sorted(glob.glob(str(C.HERE / "loss_curve_*.csv"))): + e = pd.read_csv(f).set_index("epoch") + line = [f.split("loss_curve_")[1][:20]] + for lo, hi in [(31, 40), (41, 50)]: + x = e.loc[lo:hi] + r = stats.linregress(x.index.values, x.loss_med.values) + ts = stats.theilslopes(x.loss_med.values, x.index.values) + line.append(f"ep{lo}-{hi}: level {x.loss_med.mean():.4f}, OLS slope {r.slope:+.5f}+-{r.stderr:.5f}/epoch, " + f"Theil-Sen {ts.slope:+.5f} [{ts.low_slope:+.5f},{ts.high_slope:+.5f}]") + a, b = e.loc[6:10].loss_med.mean(), e.loc[41:50].loss_med.mean() + line.append(f"median-loss level ep6-10 {a:.4f} -> ep41-50 {b:.4f} ({100 * (b - a) / a:+.1f}%); ep1 {e.loss_med.iloc[0]:.4f}") + out.append(" " + "\n ".join(line)) +open(C.HERE / "loss_robust.txt", "w").write("\n".join(out) + "\n") +print("\n".join(out)) diff --git a/experiments/levers/loss_robust.txt b/experiments/levers/loss_robust.txt new file mode 100644 index 00000000..cb0838ca --- /dev/null +++ b/experiments/levers/loss_robust.txt @@ -0,0 +1,16 @@ + 2026-09-12T03-53-34Z + ep31-40: level 0.2377, OLS slope -0.00489+-0.00209/epoch, Theil-Sen -0.00506 [-0.01139,-0.00070] + ep41-50: level 0.2406, OLS slope +0.00057+-0.00335/epoch, Theil-Sen +0.00140 [-0.00765,+0.00714] + median-loss level ep6-10 0.2636 -> ep41-50 0.2406 (-8.7%); ep1 0.2536 + 2026-09-12T23-39-06Z + ep31-40: level 0.2419, OLS slope -0.00453+-0.00217/epoch, Theil-Sen -0.00550 [-0.01156,+0.00072] + ep41-50: level 0.2438, OLS slope +0.00136+-0.00329/epoch, Theil-Sen +0.00261 [-0.00626,+0.00815] + median-loss level ep6-10 0.2613 -> ep41-50 0.2438 (-6.7%); ep1 0.2850 + 2026-09-27T07-29-47Z + ep31-40: level 0.2427, OLS slope -0.00425+-0.00231/epoch, Theil-Sen -0.00503 [-0.01238,+0.00180] + ep41-50: level 0.2469, OLS slope +0.00101+-0.00329/epoch, Theil-Sen +0.00242 [-0.00675,+0.00833] + median-loss level ep6-10 0.2703 -> ep41-50 0.2469 (-8.7%); ep1 0.2604 + 2026-09-27T10-31-30Z + ep31-40: level 0.2587, OLS slope +0.00126+-0.00242/epoch, Theil-Sen +0.00280 [-0.00436,+0.00869] + ep41-50: level 0.2447, OLS slope -0.00701+-0.00460/epoch, Theil-Sen -0.00553 [-0.01568,+0.00382] + median-loss level ep6-10 0.2474 -> ep41-50 0.2447 (-1.1%); ep1 0.2432 diff --git a/experiments/levers/make_ckpt_configs.py b/experiments/levers/make_ckpt_configs.py new file mode 100644 index 00000000..035b455b --- /dev/null +++ b/experiments/levers/make_ckpt_configs.py @@ -0,0 +1,35 @@ +"""Zero-step replay configs for the queue (see queue.sh). Each is the source run's config.yaml snapshot verbatim plus +experiment.checkpoint (a checkpoint whose sampler is exhausted: next_mini_batch 10, cursor 2365), experiment.epochs set +to that checkpoint's epoch (so Phase 1 takes zero optimizer steps), and the testing window below. + + s42_e{10,25,40}_test5y : seed 42 no-dam, intermediate checkpoints, WY1996-2000 (the source run's own first five test + years, same cold-start date, so epoch 50 is read from the source run's predictions) + g0_trainyears : the gamma = 0, n+p+q recipe (2026-09-12T03-53-34Z), last checkpoint, 1981/10/01..1996/09/30 +""" +import json +import pathlib +import re + +RUNS = pathlib.Path("/home/tbindas/projects/ddrs/.ddrs/runs") +HERE = pathlib.Path(__file__).resolve().parent +JOBS = { + "s42_e10_test5y": ("2026-09-27T07-29-47Z-train-and-test", 10, "1995/10/01", "2000/09/30"), + "s42_e25_test5y": ("2026-09-27T07-29-47Z-train-and-test", 25, "1995/10/01", "2000/09/30"), + "s42_e40_test5y": ("2026-09-27T07-29-47Z-train-and-test", 40, "1995/10/01", "2000/09/30"), + "g0_trainyears": ("2026-09-12T03-53-34Z-train-and-test", 50, "1981/10/01", "1996/09/30"), +} +for name, (rid, ep, t0, t1) in JOBS.items(): + ck = RUNS / rid / "checkpoints" / f"epoch_{ep}_mb_9" + st = json.load(open(ck / "state.json")) + assert st["epoch"] == ep and st["next_mini_batch"] == 10 and st["sampler_cursor"] == 2365, (name, st["epoch"]) + assert not (RUNS / rid / "checkpoints" / f"epoch_{ep}_mb_10").exists() + txt = (RUNS / rid / "config.yaml").read_text() + txt, k = re.subn(r"\n epochs: 50(\s)", f"\n checkpoint: {ck}\n epochs: {ep}\\1", txt, count=1) + assert k == 1, name + old_t = "testing:\n start_time: 1995/10/01\n end_time: 2010/09/30\n" + assert txt.count(old_t) == 1, name + txt = txt.replace(old_t, f"testing:\n start_time: {t0}\n end_time: {t1}\n", 1) + hdr = (f"# levers replay {name}: zero-step replay of {rid} checkpoint epoch_{ep}_mb_9, testing {t0}..{t1}.\n" + f"# Generated by experiments/levers/make_ckpt_configs.py; otherwise the run's snapshot verbatim.\n") + (HERE / f"replay_{name}.yaml").write_text(hdr + txt) + print("wrote", name) diff --git a/experiments/levers/make_plan_configs.py b/experiments/levers/make_plan_configs.py new file mode 100644 index 00000000..68d3861c --- /dev/null +++ b/experiments/levers/make_plan_configs.py @@ -0,0 +1,17 @@ +"""Configs for `ddrs plan` summed-Q' baselines over the training years WY1986-1995 (testing window 1985/10/01..1995/09/30) +for the two daily Q' stores not replayed (dHBV2 lumped AORC, HydroDL LSTM AORC). The baseline is routing-free, so the +checkpoint does not matter; the config is the store's 2026-09-13 run snapshot with only the testing window changed. +The replays of UH (s42), DIST and LSTM write the same baseline for their windows as a side effect of `ddrs run`. +""" +import pathlib + +RUNS = pathlib.Path("/home/tbindas/projects/ddrs/.ddrs/runs") +HERE = pathlib.Path(__file__).resolve().parent +for name, rid in {"lumped": "2026-09-13T13-56-50Z-train-and-test", "hydrodl": "2026-09-13T17-21-58Z-train-and-test", + "uh": "2026-09-27T07-29-47Z-train-and-test"}.items(): + txt = (RUNS / rid / "config.yaml").read_text() + old_t = "testing:\n start_time: 1995/10/01\n end_time: 2010/09/30\n" + assert txt.count(old_t) == 1, name + txt = txt.replace(old_t, "testing:\n start_time: 1985/10/01\n end_time: 1995/09/30\n", 1) + (HERE / f"plan_{name}_train10.yaml").write_text(f"# summed-Q' baseline over WY1986-1995 for {rid}'s store\n" + txt) + print("wrote", name) diff --git a/experiments/levers/make_replay_configs.py b/experiments/levers/make_replay_configs.py new file mode 100644 index 00000000..99d79f76 --- /dev/null +++ b/experiments/levers/make_replay_configs.py @@ -0,0 +1,30 @@ +"""Write zero-step replay configs that score the no-dam full-population checkpoints on the TRAINING years. + +Each config is the source run's config.yaml snapshot verbatim, plus: + experiment.checkpoint: the run's last checkpoint (epoch_50_mb_9, sampler cursor 2365 of 2365), epochs unchanged (50), + so Phase 1 takes zero optimizer steps and Phase 2 tests exactly that checkpoint (src/cli/run.rs Phase 2 fallback); + testing window 1981/10/01 .. 1996/09/30: the training years (WY1982-1995) plus WY1996, which overlaps the source run's + own test window and validates the replay against the source run's predictions. +""" +import pathlib + +RUNS = pathlib.Path("/home/tbindas/projects/ddrs/.ddrs/runs") +HERE = pathlib.Path(__file__).resolve().parent +SRC = {42: "2026-09-27T07-29-47Z-train-and-test", 43: "2026-09-27T10-31-30Z-train-and-test"} + +for seed, rid in SRC.items(): + txt = (RUNS / rid / "config.yaml").read_text() + ck = RUNS / rid / "checkpoints" / "epoch_50_mb_9" + assert (ck / "head.mpk").exists() + old_ep = " epochs: 50 #" + assert txt.count(old_ep) == 1 + txt = txt.replace(old_ep, f" checkpoint: {ck}\n epochs: 50 #", 1) + old_t = "testing:\n start_time: 1995/10/01\n end_time: 2010/09/30\n" + assert txt.count(old_t) == 1 + txt = txt.replace(old_t, "testing:\n start_time: 1981/10/01\n end_time: 1996/09/30\n", 1) + hdr = (f"# levers_replay_trainyears_s{seed}: zero-step replay of {rid} (checkpoint epoch_50_mb_9)\n" + f"# scored on 1981/10/01..1996/09/30 (training years + WY1996 overlap). Generated by\n" + f"# experiments/levers/make_replay_configs.py; everything below is the run's snapshot except those two edits.\n") + out = HERE / f"replay_trainyears_s{seed}.yaml" + out.write_text(hdr + txt) + print("wrote", out) diff --git a/experiments/levers/make_store_configs.py b/experiments/levers/make_store_configs.py new file mode 100644 index 00000000..5cf2533c --- /dev/null +++ b/experiments/levers/make_store_configs.py @@ -0,0 +1,27 @@ +"""Zero-step training-years replays of the working recipe on two other Q' stores (same config as seed 42 except the +streamflow store), for honest ensemble weights and multipliers: testing window 1985/10/01..1995/09/30. + lstm_trainyears : 2026-09-13T13-55-03Z (daily_lstm_merit_unit_catchments.ic) + dist_trainyears : 2026-09-13T17-22-30Z (daily_dhbv2_distributed_aorc2f ... _rechunked.ic) +""" +import json +import pathlib +import re + +RUNS = pathlib.Path("/home/tbindas/projects/ddrs/.ddrs/runs") +HERE = pathlib.Path(__file__).resolve().parent +for name, rid in {"lstm_trainyears": "2026-09-13T13-55-03Z-train-and-test", + "dist_trainyears": "2026-09-13T17-22-30Z-train-and-test"}.items(): + ck = RUNS / rid / "checkpoints" / "epoch_50_mb_9" + st = json.load(open(ck / "state.json")) + assert st["epoch"] == 50 and st["next_mini_batch"] == 10 and st["sampler_cursor"] == 2365, (name, st["epoch"]) + assert not (RUNS / rid / "checkpoints" / "epoch_50_mb_10").exists() + txt = (RUNS / rid / "config.yaml").read_text() + txt, k = re.subn(r"\n epochs: 50(\s)", f"\n checkpoint: {ck}\n epochs: 50\\1", txt, count=1) + assert k == 1, name + old_t = "testing:\n start_time: 1995/10/01\n end_time: 2010/09/30\n" + assert txt.count(old_t) == 1, name + txt = txt.replace(old_t, "testing:\n start_time: 1985/10/01\n end_time: 1995/09/30\n", 1) + hdr = (f"# levers replay {name}: zero-step replay of {rid} epoch_50_mb_9, testing 1985/10/01..1995/09/30.\n" + f"# Generated by experiments/levers/make_store_configs.py; otherwise the run's snapshot verbatim.\n") + (HERE / f"replay_{name}.yaml").write_text(hdr + txt) + print("wrote", name, "streamflow:", re.search(r"streamflow: (\S+)", txt).group(1)) diff --git a/experiments/levers/mult_attr_gbm_s42.npy b/experiments/levers/mult_attr_gbm_s42.npy new file mode 100644 index 0000000000000000000000000000000000000000..c1f429ba032e41bc64fd7be9c3e009d86fd818b3 GIT binary patch literal 19048 zcmbWfcRZEx`vxu{q>v~pqhv-ANw}$y>{Zz_TgpgFQIs+wt576VDkDOa?4n_`R8&Uh zInVQ)Vk{b2~Fj`(-T0Sx(D(9S-#jk=2rv z)%MnuRg#qT4h{(maX;c39O8BOKR-8g4-7qwKM(bFKYAFyms3^KP*;*yl8lu6|ND=7 z?8aI>uQ?J%A3eWktxdwm#ycueE(~y84&~({3{X-px*_CCfs@l z`t0n_f7nR4d$N0#B`XEX1hyV&uP33iWP%u%C!j&YcOc%Kge_xV5-z?VfbCI~Y)Bm) zE{mlda_yvpM9^Uo^_>)a>Flv%7pCCB@ay>TJru0r%D9x*O~H-3CI#X7bjaH5;S+R* z1V>%Z@2cMjIK8@w{S$+P3o7D;F?URQBB zk%B!<8syar6x>!gckaGA36Z0G_aD4vfHG}=Q?w`r-(?H`{*|P`>9@b-Cx$8TN;faI zQzrm&WRAqkQ1C`;jp)i83V!{bDo{|Mpybcb-F(p`$d{-lyk=v-?yy9LFuv|X7$@v( 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zaky4l;YGVT;;n^+pVtfQ=X~=jN!pIN#h;hazZ|hH^n=42ywFeV`gSW`iw1R3*SDFA z(jduPhfEeC;LP=#(Wg*f?Uj_ADn)#d`)dT9gUAyKT+Q$VaWF`RcI6S~{iXtO0SzRy z?hP?(&%m6Y#td4JLcX!0w>1^e;6mvYb2WTV6uyLe^eN)|r}Ku2VK}cBrC%R&2J1lm wRRZsLG0q)%GiyI#4!h4Zt3Uwj(z{tu;EoTzZ#_18Uk>?O<>~Jej;KriA9&@G1^@s6 literal 0 HcmV?d00001 diff --git a/experiments/levers/peek.py b/experiments/levers/peek.py new file mode 100644 index 00000000..092ec9f2 --- /dev/null +++ b/experiments/levers/peek.py @@ -0,0 +1,9 @@ +import sys +import zarr +R = sys.argv[1] if len(sys.argv) > 1 else "/home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/" + "ev" + "al/predictions.zarr" +z = zarr.open(R, mode="r") +print(list(z.keys()), dict(z.attrs)) +for k in z.keys(): + print(k, z[k].shape, z[k].dtype) +t = z["time"][:] +print(t[:3], t[-3:]) diff --git a/experiments/levers/peek_attrs.py b/experiments/levers/peek_attrs.py new file mode 100644 index 00000000..3c66ad20 --- /dev/null +++ b/experiments/levers/peek_attrs.py @@ -0,0 +1,11 @@ +import xarray as xr +import zarr +ds = xr.open_dataset("/home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc") +print(ds) +gz = zarr.open_group("/home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr", mode="r") +ks = list(gz.keys()) +print(len(ks), ks[:5]) +g = gz[ks[0]] +print(list(g.keys()), dict(g.attrs)) +for k in g.keys(): + print(k, g[k].shape, g[k].dtype, g[k][:5]) diff --git a/experiments/levers/plan_dist.log b/experiments/levers/plan_dist.log new file mode 100644 index 00000000..a7bd05c0 --- /dev/null +++ b/experiments/levers/plan_dist.log @@ -0,0 +1,24 @@ +warning: data source drift since last plan: ["streamflow"] — relocking +summed Q' gauge filter: dropped 513 headwater (matches training's dataset filter) +summed Q': 2698 gauges × 79889 unique upstream divides × 3652 days +workflow TrainAndTest +drift ["streamflow"] +adjacency + network /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gauges /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + cache (explicit paths — no managed build) + +================================================================================ + SUMMED Q' METRICS SUMMARY +================================================================================ +Total Gauges Evaluated: 2698 +-------------------------------------------------------------------------------- +METRIC MEDIAN MEAN Q25 Q75 VALID +-------------------------------------------------------------------------------- +Bias 0.986 3.155 0.251 2.638 2698 +FLV (%) 45.16 6988.63 12.13 136.38 2633 +FHV (%) 2.56 32.66 -6.66 14.35 2698 +KGE 0.762 0.112 0.600 0.846 2698 +NSE 0.730 -69.635 0.527 0.824 2698 +================================================================================ +baseline computed → /home/tbindas/projects/ddrs/.ddrs/baselines/196652afd33a6fda diff --git a/experiments/levers/plan_hydrodl.log b/experiments/levers/plan_hydrodl.log new file mode 100644 index 00000000..1769491a --- /dev/null +++ b/experiments/levers/plan_hydrodl.log @@ -0,0 +1,23 @@ +summed Q' gauge filter: dropped 513 headwater (matches training's dataset filter) +summed Q': 2698 gauges × 79889 unique upstream divides × 3652 days +workflow TrainAndTest +drift [] +adjacency + network /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gauges /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + cache (explicit paths — no managed build) + +================================================================================ + SUMMED Q' METRICS SUMMARY +================================================================================ +Total Gauges Evaluated: 2698 +-------------------------------------------------------------------------------- +METRIC MEDIAN MEAN Q25 Q75 VALID +-------------------------------------------------------------------------------- +Bias -0.761 -1.125 -4.092 0.459 2698 +FLV (%) 7.48 8104.71 -21.01 72.84 2633 +FHV (%) -8.64 49.76 -25.67 16.33 2698 +KGE 0.539 -0.598 0.320 0.657 2698 +NSE 0.446 -270.316 0.019 0.613 2698 +================================================================================ +baseline computed → /home/tbindas/projects/ddrs/.ddrs/baselines/8e6b5af4534def3c diff --git a/experiments/levers/plan_hydrodl_train10.yaml b/experiments/levers/plan_hydrodl_train10.yaml new file mode 100644 index 00000000..391d5168 --- /dev/null +++ b/experiments/levers/plan_hydrodl_train10.yaml @@ -0,0 +1,323 @@ +# summed-Q' baseline over WY1986-1995 for 2026-09-13T17-21-58Z-train-and-test's store +# sr_n0_gamma_hydrodl_lstm +# +# Inflow-source arm for the Beven-challenges study (AGU 2026): the n_0 + gamma +# head with the channel fixed (p = 21, q = 0.65), trained on the +# hydroDL LSTM, AORC forcing, daily +# unit-catchment product, everything else identical to sr_n0_gamma.yaml +# (2,365 gauges of gages_3000.csv, seed 42, 500 updates). Five such arms, all +# AORC-forced, none on the dHBV2 UH retrospective. Question: how much of the +# learned roughness is the routing absorbing inflow bias, and do the per-gauge +# loss-landscape optima move with the inflow source? +# +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/daily_hydrodl_lstm_aorc2f_merit_unit_catchments_rechunked.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1985/10/01 + end_time: 1995/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/plan_lstm.log b/experiments/levers/plan_lstm.log new file mode 100644 index 00000000..9be9c218 --- /dev/null +++ b/experiments/levers/plan_lstm.log @@ -0,0 +1,23 @@ +summed Q' gauge filter: dropped 513 headwater (matches training's dataset filter) +summed Q': 2698 gauges × 79889 unique upstream divides × 3652 days +workflow TrainAndTest +drift [] +adjacency + network /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gauges /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + cache (explicit paths — no managed build) + +================================================================================ + SUMMED Q' METRICS SUMMARY +================================================================================ +Total Gauges Evaluated: 2698 +-------------------------------------------------------------------------------- +METRIC MEDIAN MEAN Q25 Q75 VALID +-------------------------------------------------------------------------------- +Bias 0.282 1.899 -0.620 1.790 2698 +FLV (%) 41.63 13643.08 -0.86 159.64 2633 +FHV (%) 2.16 46.52 -14.98 26.09 2698 +KGE 0.655 -0.244 0.387 0.785 2698 +NSE 0.614 -132.320 0.232 0.772 2698 +================================================================================ +baseline computed → /home/tbindas/projects/ddrs/.ddrs/baselines/367784ef8974b46f diff --git a/experiments/levers/plan_lumped.log b/experiments/levers/plan_lumped.log new file mode 100644 index 00000000..0ca0efba --- /dev/null +++ b/experiments/levers/plan_lumped.log @@ -0,0 +1,24 @@ +warning: data source drift since last plan: ["streamflow"] — relocking +summed Q' gauge filter: dropped 513 headwater (matches training's dataset filter) +summed Q': 2698 gauges × 79889 unique upstream divides × 3652 days +workflow TrainAndTest +drift ["streamflow"] +adjacency + network /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gauges /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + cache (explicit paths — no managed build) + +================================================================================ + SUMMED Q' METRICS SUMMARY +================================================================================ +Total Gauges Evaluated: 2698 +-------------------------------------------------------------------------------- +METRIC MEDIAN MEAN Q25 Q75 VALID +-------------------------------------------------------------------------------- +Bias -0.335 -0.296 -2.667 0.822 2698 +FLV (%) 4.93 17630.26 -27.29 86.17 2633 +FHV (%) 15.06 69.90 -6.55 48.28 2698 +KGE 0.471 -0.571 0.183 0.638 2698 +NSE 0.122 -127.200 -0.574 0.489 2698 +================================================================================ +baseline computed → /home/tbindas/projects/ddrs/.ddrs/baselines/7b6fd0ba64be6ae2 diff --git a/experiments/levers/plan_lumped_train10.yaml b/experiments/levers/plan_lumped_train10.yaml new file mode 100644 index 00000000..ecb22363 --- /dev/null +++ b/experiments/levers/plan_lumped_train10.yaml @@ -0,0 +1,323 @@ +# summed-Q' baseline over WY1986-1995 for 2026-09-13T13-56-50Z-train-and-test's store +# sr_n0_gamma_dhbv2_lumped +# +# Inflow-source arm for the Beven-challenges study (AGU 2026): the n_0 + gamma +# head with the channel fixed (p = 21, q = 0.65), trained on the +# dHBV2 lumped, AORC forcing, daily +# unit-catchment product, everything else identical to sr_n0_gamma.yaml +# (2,365 gauges of gages_3000.csv, seed 42, 500 updates). Five such arms, all +# AORC-forced, none on the dHBV2 UH retrospective. Question: how much of the +# learned roughness is the routing absorbing inflow bias, and do the per-gauge +# loss-landscape optima move with the inflow source? +# +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/daily_dhbv2_lumped_aorc2f_merit_unit_catchments_rechunked.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1985/10/01 + end_time: 1995/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/plan_uh.log b/experiments/levers/plan_uh.log new file mode 100644 index 00000000..a965640a --- /dev/null +++ b/experiments/levers/plan_uh.log @@ -0,0 +1,24 @@ +warning: data source drift since last plan: ["streamflow"] — relocking +summed Q' gauge filter: dropped 513 headwater (matches training's dataset filter) +summed Q': 2698 gauges × 79889 unique upstream divides × 3652 days +workflow TrainAndTest +drift ["streamflow"] +adjacency + network /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gauges /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + cache (explicit paths — no managed build) + +================================================================================ + SUMMED Q' METRICS SUMMARY +================================================================================ +Total Gauges Evaluated: 2698 +-------------------------------------------------------------------------------- +METRIC MEDIAN MEAN Q25 Q75 VALID +-------------------------------------------------------------------------------- +Bias 0.737 2.122 0.097 2.052 2698 +FLV (%) 41.25 7069.40 8.10 132.37 2633 +FHV (%) 1.27 27.04 -8.90 11.43 2698 +KGE 0.770 0.195 0.608 0.853 2698 +NSE 0.734 -61.883 0.543 0.827 2698 +================================================================================ +baseline computed → /home/tbindas/projects/ddrs/.ddrs/baselines/abe830ef3781964b diff --git a/experiments/levers/plan_uh_train10.yaml b/experiments/levers/plan_uh_train10.yaml new file mode 100644 index 00000000..c6619737 --- /dev/null +++ b/experiments/levers/plan_uh_train10.yaml @@ -0,0 +1,329 @@ +# summed-Q' baseline over WY1986-1995 for 2026-09-27T07-29-47Z-train-and-test's store +# dam_release_full_off +# +# Full-population no-dam arm for the learned dam release: every reach routed as a +# channel. Matched arm: dam_release_full_learned.yaml (same seed, same config, plus +# the learned release at all 1,024 NID dam COMIDs). The pair measures the per-gauge +# change the learned release makes over the whole eval population, at gauges below a +# NID dam >= 10 MCM and at gauges without one. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_off.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1985/10/01 + end_time: 1995/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/prereg.txt b/experiments/levers/prereg.txt new file mode 100644 index 00000000..600ca167 --- /dev/null +++ b/experiments/levers/prereg.txt @@ -0,0 +1,16 @@ +Pre-registration, written 2026-09-30 ~13:35Z, BEFORE any training-year replay output existed (the seed-42 replay +2026-09-30T13-13-59Z was still in Phase 2). The smoke tests of volume.py / timing.py used an overlapping stand-in window +(WY1996-2000 as "training") and are code tests only. + +Primary runoff multipliers for the combined estimate (fit on WY1982-1995, score on WY1996-2010): + global : the median over gauges of the per-gauge least-squares scalar k* = sum(p o) / sum(p^2) + regional: per-HUC2, the median of k* within the HUC2 + attribute-conditioned: GBM on log k*, 10-fold by gauge (secondary: leave-one-HUC2-out as the stricter transfer test) + ceiling : per-gauge k* (gauged calibration only) +All other variants (max-training-median scalar, volume-ratio targets, ridge) are reported as sensitivity, not selected. + +Primary variance lever: equal-weight two-seed ensemble (s42 + s43); no weights fitted. +Primary recipe lever: gamma fixed at 0 (n-only head, 2026-09-12T23-39-06Z, same channel as the working model) and, as a + separate arm, gamma = 0 with p and q learned (2026-09-12T03-53-34Z), both single-seed. +Decision rule for "can reach +0.03": the sum of the primary honest levers' population-median changes (vs 0.7391), + checked by applying them jointly to per-gauge predictions where possible. diff --git a/experiments/levers/queue.log b/experiments/levers/queue.log new file mode 100644 index 00000000..6516dbe0 --- /dev/null +++ b/experiments/levers/queue.log @@ -0,0 +1,11 @@ +2026-09-30T13:50:59Z start s43_trainyears +2026-09-30T14:01:08Z queue replaced: waiting on 280886 (s43), then lstm_trainyears dist_trainyears s42_e25_test5y s42_e40_test5y +2026-09-30T14:30:08Z start lstm_trainyears +2026-09-30T14:56:59Z done lstm_trainyears exit=0 +2026-09-30T14:56:59Z start dist_trainyears +2026-09-30T15:22:36Z done dist_trainyears exit=0 +2026-09-30T15:22:36Z start s42_e25_test5y +2026-09-30T15:35:31Z done s42_e25_test5y exit=0 +2026-09-30T15:35:31Z start s42_e40_test5y +2026-09-30T15:48:10Z done s42_e40_test5y exit=0 +2026-09-30T15:48:10Z queue finished diff --git a/experiments/levers/queue.out b/experiments/levers/queue.out new file mode 100644 index 00000000..e69de29b diff --git a/experiments/levers/queue.sh b/experiments/levers/queue.sh new file mode 100755 index 00000000..71c7731f --- /dev/null +++ b/experiments/levers/queue.sh @@ -0,0 +1,19 @@ +#!/usr/bin/env bash +# Sequential replay queue: one ddrs run at a time, each only when >= 20 GB memory is available. +# Usage: queue.sh [ ...] (job = replay_.yaml in this directory) +HERE=/home/tbindas/projects/ddrs/.claude/worktrees/agent-a92e512a7c47c97b4/experiments/levers +BIN=/home/tbindas/.claude/jobs/dacd6d8c/tmp/bin/ddrs-v6-77aa4a0 +WAITPID="$1"; shift +cd /home/tbindas/projects/ddrs/.claude/worktrees/agent-a92e512a7c47c97b4 +while kill -0 "$WAITPID" 2>/dev/null; do sleep 20; done +for JOB in "$@"; do + while [ "$(free -g | awk '/^Mem:/{print $7}')" -lt 20 ]; do + echo "$(date -u +%FT%TZ) waiting for memory before $JOB" >> "$HERE/queue.log"; sleep 60 + done + echo "$(date -u +%FT%TZ) start $JOB" >> "$HERE/queue.log" + DDRS_JOURNAL_OFF=1 RAYON_NUM_THREADS=8 "$BIN" --config "$HERE/replay_${JOB}.yaml" \ + --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu \ + > "$HERE/replay_${JOB}.log" 2>&1 < /dev/null + echo "$(date -u +%FT%TZ) done $JOB exit=$?" >> "$HERE/queue.log" +done +echo "$(date -u +%FT%TZ) queue finished" >> "$HERE/queue.log" diff --git a/experiments/levers/queue2.out b/experiments/levers/queue2.out new file mode 100644 index 00000000..e69de29b diff --git a/experiments/levers/replay_check.py b/experiments/levers/replay_check.py new file mode 100644 index 00000000..dde76d6b --- /dev/null +++ b/experiments/levers/replay_check.py @@ -0,0 +1,43 @@ +"""Replay validation over the WY1996 overlap: how the replay (warm, started 1981-10-01) and the source run (cold start +1995-10-01) differ by date, and whether the difference decays like an initial-condition transient. +Usage: replay_check.py . Writes replay_check_.txt.""" +import sys + +import numpy as np +import pandas as pd + +import common as C + +src, rep = sys.argv[1], sys.argv[2] +ids, tt, PT, OT = C.load_preds(src) +rid, tr, PR, OR = C.load_preds(rep) +PR = C.align(rid, PR, ids) +common_d = [d for d in tt if d in set(tr)] +it = np.array([tt.get_loc(d) for d in common_d]) +ir = np.array([tr.get_loc(d) for d in common_d]) +a, b = PT[:, it], PR[:, ir] +rel = np.abs(a - b) / np.maximum(np.abs(a), 1e-3) +out = [f"overlap {common_d[0].date()}..{common_d[-1].date()} ({len(common_d)} d)"] +dates = pd.DatetimeIndex(common_d) +for d0 in ["1995-10-02", "1995-10-15", "1995-11-01", "1995-12-01", "1996-01-01", "1996-03-01", "1996-06-01", "1996-09-01"]: + k = dates.get_indexer([pd.Timestamp(d0)], method="nearest")[0] + out.append(f" {d0}: median rel diff {np.nanmedian(rel[:, k]):.2e}, p90 {np.nanpercentile(rel[:, k], 90):.2e}, " + f"p99 {np.nanpercentile(rel[:, k], 99):.2e}, share > 1e-3 {np.nanmean(rel[:, k] > 1e-3):.3f}") +# volume over the overlap after Jan 1 +m = dates >= pd.Timestamp("1996-01-01") +va, vb = np.nansum(a[:, m], 1), np.nansum(b[:, m], 1) +r = vb / va +out.append(f" Jan-Sep 1996 volume replay/source: median {np.nanmedian(r):.5f}, p1 {np.nanpercentile(r, 1):.4f}, " + f"p99 {np.nanpercentile(r, 99):.4f}, share |r-1| > 1e-3 {np.nanmean(np.abs(r - 1) > 1e-3):.3f}") +na = C.metrics(a[:, m], OT[:, it][:, m], min_days=200).nse.values +nb = C.metrics(b[:, m], OT[:, it][:, m], min_days=200).nse.values +d = np.abs(na - nb) +out.append(f" Jan-Sep 1996 NSE |source - replay|: median {np.nanmedian(d):.2e}, p99 {np.nanpercentile(d, 99):.2e}, " + f"max {np.nanmax(d):.2e}, gauges > 0.01: {int(np.nansum(d > 0.01))}") +worst = np.argsort(-np.nan_to_num(d))[:5] +G = pd.read_csv(C.HERE / "gauges_table.csv", dtype={"STAID": str}).set_index("STAID").reindex(ids) +for w in worst: + out.append(f" {ids[w]} area {G.DRAIN_SQKM.iloc[w]:.0f} km2 NSE source {na[w]:.3f} replay {nb[w]:.3f}; " + f"mean flow source {np.nanmean(a[w, m]):.3f} replay {np.nanmean(b[w, m]):.3f}") +open(C.HERE / f"replay_check_{rep[:20]}.txt", "w").write("\n".join(out) + "\n") +print("\n".join(out)) diff --git a/experiments/levers/replay_check_2026-09-30T13-13-59Z.txt b/experiments/levers/replay_check_2026-09-30T13-13-59Z.txt new file mode 100644 index 00000000..cf26b72e --- /dev/null +++ b/experiments/levers/replay_check_2026-09-30T13-13-59Z.txt @@ -0,0 +1,16 @@ +overlap 1995-10-02..1996-09-29 (364 d) + 1995-10-02: median rel diff 7.66e-03, p90 1.28e-01, p99 5.99e-01, share > 1e-3 0.731 + 1995-10-15: median rel diff 4.32e-04, p90 4.37e-03, p99 2.59e-02, share > 1e-3 0.316 + 1995-11-01: median rel diff 3.48e-04, p90 3.19e-03, p99 1.62e-02, share > 1e-3 0.314 + 1995-12-01: median rel diff 2.27e-04, p90 1.65e-03, p99 7.28e-03, share > 1e-3 0.182 + 1996-01-01: median rel diff 1.18e-04, p90 1.43e-03, p99 7.79e-03, share > 1e-3 0.147 + 1996-03-01: median rel diff 1.25e-04, p90 2.55e-03, p99 1.16e-02, share > 1e-3 0.215 + 1996-06-01: median rel diff 3.55e-07, p90 2.39e-04, p99 4.48e-03, share > 1e-3 0.046 + 1996-09-01: median rel diff 8.83e-08, p90 4.94e-05, p99 9.87e-04, share > 1e-3 0.010 + Jan-Sep 1996 volume replay/source: median 1.00000, p1 0.9986, p99 1.0017, share |r-1| > 1e-3 0.033 + Jan-Sep 1996 NSE |source - replay|: median 2.19e-05, p99 1.15e-02, max 1.04e+02, gauges > 0.01: 25 + 08177500 area 1295 km2 NSE source -12655.304 replay -12758.926; mean flow source 0.895 replay 0.899 + 08198500 area 624 km2 NSE source -758.779 replay -758.969; mean flow source 0.206 replay 0.206 + 07195500 area 1630 km2 NSE source 0.410 replay 0.258; mean flow source 11.396 replay 11.088 + 07196500 area 2454 km2 NSE source 0.310 replay 0.224; mean flow source 14.879 replay 14.680 + 09169500 area 5233 km2 NSE source -196.222 replay -196.299; mean flow source 6.307 replay 6.307 diff --git a/experiments/levers/replay_check_2026-09-30T13-50-59Z.txt b/experiments/levers/replay_check_2026-09-30T13-50-59Z.txt new file mode 100644 index 00000000..769d9cf0 --- /dev/null +++ b/experiments/levers/replay_check_2026-09-30T13-50-59Z.txt @@ -0,0 +1,16 @@ +overlap 1995-10-02..1996-09-29 (364 d) + 1995-10-02: median rel diff 1.00e-02, p90 1.65e-01, p99 8.58e-01, share > 1e-3 0.730 + 1995-10-15: median rel diff 4.42e-04, p90 5.04e-03, p99 3.49e-02, share > 1e-3 0.317 + 1995-11-01: median rel diff 3.57e-04, p90 3.31e-03, p99 1.85e-02, share > 1e-3 0.309 + 1995-12-01: median rel diff 2.24e-04, p90 1.66e-03, p99 8.52e-03, share > 1e-3 0.184 + 1996-01-01: median rel diff 1.34e-04, p90 1.50e-03, p99 9.95e-03, share > 1e-3 0.155 + 1996-03-01: median rel diff 1.14e-04, p90 2.63e-03, p99 1.24e-02, share > 1e-3 0.214 + 1996-06-01: median rel diff 3.55e-07, p90 2.73e-04, p99 6.57e-03, share > 1e-3 0.052 + 1996-09-01: median rel diff 1.04e-07, p90 1.12e-04, p99 1.65e-03, share > 1e-3 0.017 + Jan-Sep 1996 volume replay/source: median 1.00000, p1 0.9986, p99 1.0014, share |r-1| > 1e-3 0.033 + Jan-Sep 1996 NSE |source - replay|: median 2.07e-05, p99 1.27e-02, max 7.98e+01, gauges > 0.01: 28 + 08177500 area 1295 km2 NSE source -12128.083 replay -12207.846; mean flow source 0.874 replay 0.878 + 08198500 area 624 km2 NSE source -690.867 replay -691.246; mean flow source 0.198 replay 0.198 + 07184000 area 511 km2 NSE source 0.334 replay 0.192; mean flow source 2.058 replay 1.953 + 07195500 area 1630 km2 NSE source 0.446 replay 0.315; mean flow source 11.374 replay 11.100 + 07196500 area 2454 km2 NSE source 0.317 replay 0.202; mean flow source 14.852 replay 14.577 diff --git a/experiments/levers/replay_dist_trainyears.log b/experiments/levers/replay_dist_trainyears.log new file mode 100644 index 00000000..c541fe57 --- /dev/null +++ b/experiments/levers/replay_dist_trainyears.log @@ -0,0 +1,264 @@ +warning: data source drift since last plan: ["streamflow"] — relocking +run output → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T14-56-59Z-train-and-test +[2026-09-30T14:56:59Z] backend: cpu (NdArray, deterministic; sparse_solver forced to cpu) +[2026-09-30T14:56:59Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T14:56:59Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T14:56:59Z] streamflow resolution: Daily +[2026-09-30T14:56:59Z] optimizer: Adam +[2026-09-30T14:56:59Z] warm start: loaded KAN head from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-13T17-22-30Z-train-and-test/checkpoints/epoch_50_mb_9/head +[2026-09-30T14:56:59Z] warm start: restored Adam state from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-13T17-22-30Z-train-and-test/checkpoints/epoch_50_mb_9/optim.mpk +[2026-09-30T14:56:59Z] warm start: resuming at epoch 50 mb 10 (rng + sampler restored) +[2026-09-30T14:56:59Z] epoch 50 lr=0.001 +[2026-09-30T14:56:59Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T14:56:59Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T14:56:59Z] streamflow resolution: Daily +[2026-09-30T14:56:59Z] Phase 1 took no optimizer step and wrote no checkpoint; testing the resumed checkpoint /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-13T17-22-30Z-train-and-test/checkpoints/epoch_50_mb_9 +[2026-09-30T14:57:07Z] chunk 1/244: days 0..15 (15 days) +[2026-09-30T14:57:14Z] chunk 2/244: days 15..30 (15 days) +[2026-09-30T14:57:19Z] chunk 3/244: days 30..45 (15 days) +[2026-09-30T14:57:26Z] chunk 4/244: days 45..60 (15 days) +[2026-09-30T14:57:32Z] chunk 5/244: days 60..75 (15 days) +[2026-09-30T14:57:38Z] chunk 6/244: days 75..90 (15 days) +[2026-09-30T14:57:45Z] chunk 7/244: days 90..105 (15 days) +[2026-09-30T14:57:52Z] chunk 8/244: days 105..120 (15 days) +[2026-09-30T14:57:58Z] chunk 9/244: days 120..135 (15 days) +[2026-09-30T14:58:04Z] chunk 10/244: days 135..150 (15 days) +[2026-09-30T14:58:10Z] chunk 11/244: days 150..165 (15 days) +[2026-09-30T14:58:17Z] chunk 12/244: days 165..180 (15 days) +[2026-09-30T14:58:23Z] chunk 13/244: days 180..195 (15 days) 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+[2026-09-30T15:20:55Z] chunk 231/244: days 3450..3465 (15 days) +[2026-09-30T15:21:01Z] chunk 232/244: days 3465..3480 (15 days) +[2026-09-30T15:21:08Z] chunk 233/244: days 3480..3495 (15 days) +[2026-09-30T15:21:14Z] chunk 234/244: days 3495..3510 (15 days) +[2026-09-30T15:21:21Z] chunk 235/244: days 3510..3525 (15 days) +[2026-09-30T15:21:27Z] chunk 236/244: days 3525..3540 (15 days) +[2026-09-30T15:21:33Z] chunk 237/244: days 3540..3555 (15 days) +[2026-09-30T15:21:39Z] chunk 238/244: days 3555..3570 (15 days) +[2026-09-30T15:21:46Z] chunk 239/244: days 3570..3585 (15 days) +[2026-09-30T15:21:52Z] chunk 240/244: days 3585..3600 (15 days) +[2026-09-30T15:21:58Z] chunk 241/244: days 3600..3615 (15 days) +[2026-09-30T15:22:04Z] chunk 242/244: days 3615..3630 (15 days) +[2026-09-30T15:22:10Z] chunk 243/244: days 3630..3645 (15 days) +[2026-09-30T15:22:15Z] chunk 244/244: days 3645..3652 (7 days) +[2026-09-30T15:22:36Z] gauges with finite NSE: 2365 / 2365 +[2026-09-30T15:22:36Z] median NSE (finite only): 0.7799 +[2026-09-30T15:22:36Z] median KGE (finite only): 0.7898 +[2026-09-30T15:22:36Z] baseline → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T14-56-59Z-train-and-test/baseline +run complete → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T14-56-59Z-train-and-test diff --git a/experiments/levers/replay_dist_trainyears.yaml b/experiments/levers/replay_dist_trainyears.yaml new file mode 100644 index 00000000..822e5553 --- /dev/null +++ b/experiments/levers/replay_dist_trainyears.yaml @@ -0,0 +1,325 @@ +# levers replay dist_trainyears: zero-step replay of 2026-09-13T17-22-30Z-train-and-test epoch_50_mb_9, testing 1985/10/01..1995/09/30. +# Generated by experiments/levers/make_store_configs.py; otherwise the run's snapshot verbatim. +# sr_n0_gamma_dhbv2_dist +# +# Inflow-source arm for the Beven-challenges study (AGU 2026): the n_0 + gamma +# head with the channel fixed (p = 21, q = 0.65), trained on the +# dHBV2 distributed, AORC forcing, daily +# unit-catchment product, everything else identical to sr_n0_gamma.yaml +# (2,365 gauges of gages_3000.csv, seed 42, 500 updates). Five such arms, all +# AORC-forced, none on the dHBV2 UH retrospective. Question: how much of the +# learned roughness is the routing absorbing inflow bias, and do the per-gauge +# loss-landscape optima move with the inflow source? +# +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/daily_dhbv2_distributed_aorc2f_merit_unit_catchments_rechunked.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + checkpoint: /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-13T17-22-30Z-train-and-test/checkpoints/epoch_50_mb_9 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1985/10/01 + end_time: 1995/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/replay_g0_trainyears.yaml b/experiments/levers/replay_g0_trainyears.yaml new file mode 100644 index 00000000..1960d84b --- /dev/null +++ b/experiments/levers/replay_g0_trainyears.yaml @@ -0,0 +1,294 @@ +# levers replay g0_trainyears: zero-step replay of 2026-09-12T03-53-34Z-train-and-test checkpoint epoch_50_mb_9, testing 1981/10/01..1996/09/30. +# Generated by experiments/levers/make_ckpt_configs.py; otherwise the run's snapshot verbatim. +# sr_gamma_0p0 +# +# Stage-dependent roughness arm, gamma = 0.0. Identical to +# head_shared_linear.yaml in every other respect, so the pair is matched. +# n(d) = n_0*(d/d_ref)^(-gamma); gamma = 0 is byte-identical to the +# historical solver (tests/stage_roughness.rs::gamma_zero_is_bit_identical). +# Design: docs/superpowers/specs/2026-09-12-stage-dependent-roughness-design.md +# +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + checkpoint: /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-12T03-53-34Z-train-and-test/checkpoints/epoch_50_mb_9 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - p_spatial + - q_spatial + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1981/10/01 + end_time: 1996/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/replay_lstm_trainyears.log b/experiments/levers/replay_lstm_trainyears.log new file mode 100644 index 00000000..b8a4a620 --- /dev/null +++ b/experiments/levers/replay_lstm_trainyears.log @@ -0,0 +1,264 @@ +warning: data source drift since last plan: ["streamflow"] — relocking +run output → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T14-30-08Z-train-and-test +[2026-09-30T14:30:08Z] backend: cpu (NdArray, deterministic; sparse_solver forced to cpu) +[2026-09-30T14:30:08Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T14:30:09Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T14:30:09Z] streamflow resolution: Daily +[2026-09-30T14:30:09Z] optimizer: Adam +[2026-09-30T14:30:09Z] warm start: loaded KAN head from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-13T13-55-03Z-train-and-test/checkpoints/epoch_50_mb_9/head +[2026-09-30T14:30:09Z] warm start: restored Adam state from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-13T13-55-03Z-train-and-test/checkpoints/epoch_50_mb_9/optim.mpk +[2026-09-30T14:30:09Z] warm start: resuming at epoch 50 mb 10 (rng + sampler restored) +[2026-09-30T14:30:09Z] epoch 50 lr=0.001 +[2026-09-30T14:30:09Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T14:30:09Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T14:30:09Z] streamflow resolution: Daily +[2026-09-30T14:30:09Z] Phase 1 took no optimizer step and wrote no checkpoint; testing the resumed checkpoint /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-13T13-55-03Z-train-and-test/checkpoints/epoch_50_mb_9 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3270..3285 (15 days) +[2026-09-30T14:54:10Z] chunk 220/244: days 3285..3300 (15 days) +[2026-09-30T14:54:17Z] chunk 221/244: days 3300..3315 (15 days) +[2026-09-30T14:54:23Z] chunk 222/244: days 3315..3330 (15 days) +[2026-09-30T14:54:29Z] chunk 223/244: days 3330..3345 (15 days) +[2026-09-30T14:54:35Z] chunk 224/244: days 3345..3360 (15 days) +[2026-09-30T14:54:42Z] chunk 225/244: days 3360..3375 (15 days) +[2026-09-30T14:54:48Z] chunk 226/244: days 3375..3390 (15 days) +[2026-09-30T14:54:54Z] chunk 227/244: days 3390..3405 (15 days) +[2026-09-30T14:55:00Z] chunk 228/244: days 3405..3420 (15 days) +[2026-09-30T14:55:07Z] chunk 229/244: days 3420..3435 (15 days) +[2026-09-30T14:55:13Z] chunk 230/244: days 3435..3450 (15 days) +[2026-09-30T14:55:19Z] chunk 231/244: days 3450..3465 (15 days) +[2026-09-30T14:55:25Z] chunk 232/244: days 3465..3480 (15 days) +[2026-09-30T14:55:31Z] chunk 233/244: days 3480..3495 (15 days) +[2026-09-30T14:55:37Z] chunk 234/244: days 3495..3510 (15 days) +[2026-09-30T14:55:43Z] chunk 235/244: days 3510..3525 (15 days) +[2026-09-30T14:55:50Z] chunk 236/244: days 3525..3540 (15 days) +[2026-09-30T14:55:56Z] chunk 237/244: days 3540..3555 (15 days) +[2026-09-30T14:56:02Z] chunk 238/244: days 3555..3570 (15 days) +[2026-09-30T14:56:09Z] chunk 239/244: days 3570..3585 (15 days) +[2026-09-30T14:56:15Z] chunk 240/244: days 3585..3600 (15 days) +[2026-09-30T14:56:22Z] chunk 241/244: days 3600..3615 (15 days) +[2026-09-30T14:56:28Z] chunk 242/244: days 3615..3630 (15 days) +[2026-09-30T14:56:34Z] chunk 243/244: days 3630..3645 (15 days) +[2026-09-30T14:56:38Z] chunk 244/244: days 3645..3652 (7 days) +[2026-09-30T14:56:58Z] gauges with finite NSE: 2365 / 2365 +[2026-09-30T14:56:58Z] median NSE (finite only): 0.7076 +[2026-09-30T14:56:58Z] median KGE (finite only): 0.6921 +[2026-09-30T14:56:58Z] baseline → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T14-30-08Z-train-and-test/baseline +run complete → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T14-30-08Z-train-and-test diff --git a/experiments/levers/replay_lstm_trainyears.yaml b/experiments/levers/replay_lstm_trainyears.yaml new file mode 100644 index 00000000..b59bd069 --- /dev/null +++ b/experiments/levers/replay_lstm_trainyears.yaml @@ -0,0 +1,325 @@ +# levers replay lstm_trainyears: zero-step replay of 2026-09-13T13-55-03Z-train-and-test epoch_50_mb_9, testing 1985/10/01..1995/09/30. +# Generated by experiments/levers/make_store_configs.py; otherwise the run's snapshot verbatim. +# sr_n0_gamma_nh_daily_lstm +# +# Inflow-source arm for the Beven-challenges study (AGU 2026): the n_0 + gamma +# head with the channel fixed (p = 21, q = 0.65), trained on the +# NeuralHydrology daily LSTM +# unit-catchment product, everything else identical to sr_n0_gamma.yaml +# (2,365 gauges of gages_3000.csv, seed 42, 500 updates). Five such arms, all +# AORC-forced, none on the dHBV2 UH retrospective. Question: how much of the +# learned roughness is the routing absorbing inflow bias, and do the per-gauge +# loss-landscape optima move with the inflow source? +# +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/daily_lstm_merit_unit_catchments.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + checkpoint: /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-13T13-55-03Z-train-and-test/checkpoints/epoch_50_mb_9 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1985/10/01 + end_time: 1995/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/replay_s42.log b/experiments/levers/replay_s42.log new file mode 100644 index 00000000..4dc7d650 --- /dev/null +++ b/experiments/levers/replay_s42.log @@ -0,0 +1,388 @@ +warning: data source drift since last plan: ["reservoirs"] — relocking +summed Q' gauge filter: dropped 513 headwater (matches training's dataset filter) +summed Q': 2698 gauges × 79889 unique upstream divides × 5479 days +run output → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T13-13-59Z-train-and-test +[2026-09-30T13:13:59Z] backend: cpu (NdArray, deterministic; sparse_solver forced to cpu) +[2026-09-30T13:13:59Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T13:13:59Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T13:13:59Z] streamflow resolution: Daily +[2026-09-30T13:13:59Z] optimizer: Adam +[2026-09-30T13:13:59Z] warm start: loaded KAN head from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_50_mb_9/head +[2026-09-30T13:13:59Z] warm start: restored Adam state from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_50_mb_9/optim.mpk +[2026-09-30T13:13:59Z] warm start: resuming at epoch 50 mb 10 (rng + sampler restored) +[2026-09-30T13:13:59Z] epoch 50 lr=0.001 +[2026-09-30T13:13:59Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T13:13:59Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T13:13:59Z] streamflow resolution: Daily +[2026-09-30T13:13:59Z] Phase 1 took no optimizer step and wrote no checkpoint; testing the resumed checkpoint 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(15 days) +[2026-09-30T13:25:34Z] chunk 126/366: days 1875..1890 (15 days) +[2026-09-30T13:25:40Z] chunk 127/366: days 1890..1905 (15 days) +[2026-09-30T13:25:46Z] chunk 128/366: days 1905..1920 (15 days) +[2026-09-30T13:25:51Z] chunk 129/366: days 1920..1935 (15 days) +[2026-09-30T13:25:57Z] chunk 130/366: days 1935..1950 (15 days) +[2026-09-30T13:26:03Z] chunk 131/366: days 1950..1965 (15 days) +[2026-09-30T13:26:08Z] chunk 132/366: days 1965..1980 (15 days) +[2026-09-30T13:26:14Z] chunk 133/366: days 1980..1995 (15 days) +[2026-09-30T13:26:19Z] chunk 134/366: days 1995..2010 (15 days) +[2026-09-30T13:26:25Z] chunk 135/366: days 2010..2025 (15 days) +[2026-09-30T13:26:30Z] chunk 136/366: days 2025..2040 (15 days) +[2026-09-30T13:26:36Z] chunk 137/366: days 2040..2055 (15 days) +[2026-09-30T13:26:42Z] chunk 138/366: days 2055..2070 (15 days) +[2026-09-30T13:26:47Z] chunk 139/366: days 2070..2085 (15 days) +[2026-09-30T13:26:53Z] chunk 140/366: days 2085..2100 (15 days) 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/home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T13-13-59Z-train-and-test/baseline +run complete → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T13-13-59Z-train-and-test diff --git a/experiments/levers/replay_s42_e10_test5y.yaml b/experiments/levers/replay_s42_e10_test5y.yaml new file mode 100644 index 00000000..0e96f725 --- /dev/null +++ b/experiments/levers/replay_s42_e10_test5y.yaml @@ -0,0 +1,331 @@ +# levers replay s42_e10_test5y: zero-step replay of 2026-09-27T07-29-47Z-train-and-test checkpoint epoch_10_mb_9, testing 1995/10/01..2000/09/30. +# Generated by experiments/levers/make_ckpt_configs.py; otherwise the run's snapshot verbatim. +# dam_release_full_off +# +# Full-population no-dam arm for the learned dam release: every reach routed as a +# channel. Matched arm: dam_release_full_learned.yaml (same seed, same config, plus +# the learned release at all 1,024 NID dam COMIDs). The pair measures the per-gauge +# change the learned release makes over the whole eval population, at gauges below a +# NID dam >= 10 MCM and at gauges without one. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_off.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + checkpoint: /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_10_mb_9 + epochs: 10 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1995/10/01 + end_time: 2000/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/replay_s42_e25_test5y.log b/experiments/levers/replay_s42_e25_test5y.log new file mode 100644 index 00000000..3e2b1980 --- /dev/null +++ b/experiments/levers/replay_s42_e25_test5y.log @@ -0,0 +1,144 @@ +warning: data source drift since last plan: ["streamflow"] — relocking +summed Q' gauge filter: dropped 513 headwater (matches training's dataset filter) +summed Q': 2698 gauges × 79889 unique upstream divides × 1827 days +run output → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T15-22-40Z-train-and-test +[2026-09-30T15:22:40Z] backend: cpu (NdArray, deterministic; sparse_solver forced to cpu) +[2026-09-30T15:22:40Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T15:22:40Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T15:22:40Z] streamflow resolution: Daily +[2026-09-30T15:22:40Z] optimizer: Adam +[2026-09-30T15:22:40Z] warm start: loaded KAN head from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_25_mb_9/head +[2026-09-30T15:22:40Z] warm start: restored Adam state from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_25_mb_9/optim.mpk +[2026-09-30T15:22:40Z] warm start: resuming at epoch 25 mb 10 (rng + sampler restored) +[2026-09-30T15:22:40Z] epoch 25 lr=0.002 +[2026-09-30T15:22:40Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T15:22:41Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T15:22:41Z] streamflow resolution: Daily +[2026-09-30T15:22:41Z] Phase 1 took no optimizer step and wrote no checkpoint; testing the resumed checkpoint /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_25_mb_9 +[2026-09-30T15:22:48Z] chunk 1/122: days 0..15 (15 days) +[2026-09-30T15:22:54Z] chunk 2/122: days 15..30 (15 days) +[2026-09-30T15:23:01Z] chunk 3/122: days 30..45 (15 days) +[2026-09-30T15:23:08Z] chunk 4/122: days 45..60 (15 days) +[2026-09-30T15:23:15Z] chunk 5/122: days 60..75 (15 days) +[2026-09-30T15:23:22Z] chunk 6/122: days 75..90 (15 days) +[2026-09-30T15:23:29Z] chunk 7/122: days 90..105 (15 days) +[2026-09-30T15:23:36Z] chunk 8/122: days 105..120 (15 days) +[2026-09-30T15:23:43Z] chunk 9/122: days 120..135 (15 days) +[2026-09-30T15:23:51Z] chunk 10/122: days 135..150 (15 days) +[2026-09-30T15:23:57Z] chunk 11/122: days 150..165 (15 days) +[2026-09-30T15:24:04Z] chunk 12/122: days 165..180 (15 days) +[2026-09-30T15:24:11Z] chunk 13/122: days 180..195 (15 days) +[2026-09-30T15:24:17Z] chunk 14/122: days 195..210 (15 days) +[2026-09-30T15:24:24Z] chunk 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+[2026-09-30T15:35:31Z] baseline → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T15-22-40Z-train-and-test/baseline +run complete → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T15-22-40Z-train-and-test diff --git a/experiments/levers/replay_s42_e25_test5y.yaml b/experiments/levers/replay_s42_e25_test5y.yaml new file mode 100644 index 00000000..c0836bcb --- /dev/null +++ b/experiments/levers/replay_s42_e25_test5y.yaml @@ -0,0 +1,331 @@ +# levers replay s42_e25_test5y: zero-step replay of 2026-09-27T07-29-47Z-train-and-test checkpoint epoch_25_mb_9, testing 1995/10/01..2000/09/30. +# Generated by experiments/levers/make_ckpt_configs.py; otherwise the run's snapshot verbatim. +# dam_release_full_off +# +# Full-population no-dam arm for the learned dam release: every reach routed as a +# channel. Matched arm: dam_release_full_learned.yaml (same seed, same config, plus +# the learned release at all 1,024 NID dam COMIDs). The pair measures the per-gauge +# change the learned release makes over the whole eval population, at gauges below a +# NID dam >= 10 MCM and at gauges without one. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_off.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + checkpoint: /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_25_mb_9 + epochs: 25 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1995/10/01 + end_time: 2000/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/replay_s42_e40_test5y.log b/experiments/levers/replay_s42_e40_test5y.log new file mode 100644 index 00000000..c1bbcb35 --- /dev/null +++ b/experiments/levers/replay_s42_e40_test5y.log @@ -0,0 +1,141 @@ +run output → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T15-35-31Z-train-and-test +[2026-09-30T15:35:31Z] backend: cpu (NdArray, deterministic; sparse_solver forced to cpu) +[2026-09-30T15:35:31Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T15:35:31Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T15:35:31Z] streamflow resolution: Daily +[2026-09-30T15:35:31Z] optimizer: Adam +[2026-09-30T15:35:31Z] warm start: loaded KAN head from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_40_mb_9/head +[2026-09-30T15:35:31Z] warm start: restored Adam state from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_40_mb_9/optim.mpk +[2026-09-30T15:35:31Z] warm start: resuming at epoch 40 mb 10 (rng + sampler restored) +[2026-09-30T15:35:31Z] epoch 40 lr=0.002 +[2026-09-30T15:35:31Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T15:35:32Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T15:35:32Z] streamflow resolution: Daily +[2026-09-30T15:35:32Z] Phase 1 took no optimizer step and wrote no checkpoint; testing the resumed checkpoint /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_40_mb_9 +[2026-09-30T15:35:39Z] chunk 1/122: days 0..15 (15 days) +[2026-09-30T15:35:45Z] chunk 2/122: days 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+[2026-09-30T15:45:37Z] chunk 98/122: days 1455..1470 (15 days) +[2026-09-30T15:45:43Z] chunk 99/122: days 1470..1485 (15 days) +[2026-09-30T15:45:49Z] chunk 100/122: days 1485..1500 (15 days) +[2026-09-30T15:45:55Z] chunk 101/122: days 1500..1515 (15 days) +[2026-09-30T15:46:02Z] chunk 102/122: days 1515..1530 (15 days) +[2026-09-30T15:46:08Z] chunk 103/122: days 1530..1545 (15 days) +[2026-09-30T15:46:14Z] chunk 104/122: days 1545..1560 (15 days) +[2026-09-30T15:46:20Z] chunk 105/122: days 1560..1575 (15 days) +[2026-09-30T15:46:26Z] chunk 106/122: days 1575..1590 (15 days) +[2026-09-30T15:46:33Z] chunk 107/122: days 1590..1605 (15 days) +[2026-09-30T15:46:39Z] chunk 108/122: days 1605..1620 (15 days) +[2026-09-30T15:46:45Z] chunk 109/122: days 1620..1635 (15 days) +[2026-09-30T15:46:51Z] chunk 110/122: days 1635..1650 (15 days) +[2026-09-30T15:46:58Z] chunk 111/122: days 1650..1665 (15 days) +[2026-09-30T15:47:03Z] chunk 112/122: days 1665..1680 (15 days) +[2026-09-30T15:47:10Z] chunk 113/122: days 1680..1695 (15 days) +[2026-09-30T15:47:16Z] chunk 114/122: days 1695..1710 (15 days) +[2026-09-30T15:47:22Z] chunk 115/122: days 1710..1725 (15 days) +[2026-09-30T15:47:28Z] chunk 116/122: days 1725..1740 (15 days) +[2026-09-30T15:47:35Z] chunk 117/122: days 1740..1755 (15 days) +[2026-09-30T15:47:41Z] chunk 118/122: days 1755..1770 (15 days) +[2026-09-30T15:47:47Z] chunk 119/122: days 1770..1785 (15 days) +[2026-09-30T15:47:53Z] chunk 120/122: days 1785..1800 (15 days) +[2026-09-30T15:48:00Z] chunk 121/122: days 1800..1815 (15 days) +[2026-09-30T15:48:06Z] chunk 122/122: days 1815..1827 (12 days) +[2026-09-30T15:48:10Z] gauges with finite NSE: 2365 / 2365 +[2026-09-30T15:48:10Z] median NSE (finite only): 0.7471 +[2026-09-30T15:48:10Z] median KGE (finite only): 0.7556 +[2026-09-30T15:48:10Z] baseline → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T15-35-31Z-train-and-test/baseline +run complete → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T15-35-31Z-train-and-test diff --git a/experiments/levers/replay_s42_e40_test5y.yaml b/experiments/levers/replay_s42_e40_test5y.yaml new file mode 100644 index 00000000..524e4f5f --- /dev/null +++ b/experiments/levers/replay_s42_e40_test5y.yaml @@ -0,0 +1,331 @@ +# levers replay s42_e40_test5y: zero-step replay of 2026-09-27T07-29-47Z-train-and-test checkpoint epoch_40_mb_9, testing 1995/10/01..2000/09/30. +# Generated by experiments/levers/make_ckpt_configs.py; otherwise the run's snapshot verbatim. +# dam_release_full_off +# +# Full-population no-dam arm for the learned dam release: every reach routed as a +# channel. Matched arm: dam_release_full_learned.yaml (same seed, same config, plus +# the learned release at all 1,024 NID dam COMIDs). The pair measures the per-gauge +# change the learned release makes over the whole eval population, at gauges below a +# NID dam >= 10 MCM and at gauges without one. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_off.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + checkpoint: /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_40_mb_9 + epochs: 40 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1995/10/01 + end_time: 2000/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/replay_s43_trainyears.log b/experiments/levers/replay_s43_trainyears.log new file mode 100644 index 00000000..e38a0ff3 --- /dev/null +++ b/experiments/levers/replay_s43_trainyears.log @@ -0,0 +1,385 @@ +run output → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T13-50-59Z-train-and-test +[2026-09-30T13:50:59Z] backend: cpu (NdArray, deterministic; sparse_solver forced to cpu) +[2026-09-30T13:50:59Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T13:50:59Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T13:50:59Z] streamflow resolution: Daily +[2026-09-30T13:50:59Z] optimizer: Adam +[2026-09-30T13:50:59Z] warm start: loaded KAN head from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T10-31-30Z-train-and-test/checkpoints/epoch_50_mb_9/head +[2026-09-30T13:50:59Z] warm start: restored Adam state from /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T10-31-30Z-train-and-test/checkpoints/epoch_50_mb_9/optim.mpk +[2026-09-30T13:50:59Z] warm start: resuming at epoch 50 mb 10 (rng + sampler restored) +[2026-09-30T13:50:59Z] epoch 50 lr=0.001 +[2026-09-30T13:51:00Z] DA_VALID filter: kept 2859/3211 gauges +[2026-09-30T13:51:00Z] gages_adjacency filter: kept 2365 gauges (dropped 0 missing, 494 headwater) +[2026-09-30T13:51:00Z] streamflow resolution: Daily +[2026-09-30T13:51:00Z] Phase 1 took no optimizer step and wrote no checkpoint; testing the resumed checkpoint /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T10-31-30Z-train-and-test/checkpoints/epoch_50_mb_9 +[2026-09-30T13:51:07Z] chunk 1/366: days 0..15 (15 days) +[2026-09-30T13:51:13Z] chunk 2/366: days 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chunk 343/366: days 5130..5145 (15 days) +[2026-09-30T14:27:08Z] chunk 344/366: days 5145..5160 (15 days) +[2026-09-30T14:27:15Z] chunk 345/366: days 5160..5175 (15 days) +[2026-09-30T14:27:20Z] chunk 346/366: days 5175..5190 (15 days) +[2026-09-30T14:27:27Z] chunk 347/366: days 5190..5205 (15 days) +[2026-09-30T14:27:33Z] chunk 348/366: days 5205..5220 (15 days) +[2026-09-30T14:27:39Z] chunk 349/366: days 5220..5235 (15 days) +[2026-09-30T14:27:45Z] chunk 350/366: days 5235..5250 (15 days) +[2026-09-30T14:27:51Z] chunk 351/366: days 5250..5265 (15 days) +[2026-09-30T14:27:58Z] chunk 352/366: days 5265..5280 (15 days) +[2026-09-30T14:28:04Z] chunk 353/366: days 5280..5295 (15 days) +[2026-09-30T14:28:09Z] chunk 354/366: days 5295..5310 (15 days) +[2026-09-30T14:28:16Z] chunk 355/366: days 5310..5325 (15 days) +[2026-09-30T14:28:22Z] chunk 356/366: days 5325..5340 (15 days) +[2026-09-30T14:28:28Z] chunk 357/366: days 5340..5355 (15 days) +[2026-09-30T14:28:35Z] chunk 358/366: days 5355..5370 (15 days) +[2026-09-30T14:28:41Z] chunk 359/366: days 5370..5385 (15 days) +[2026-09-30T14:28:47Z] chunk 360/366: days 5385..5400 (15 days) +[2026-09-30T14:28:53Z] chunk 361/366: days 5400..5415 (15 days) +[2026-09-30T14:28:59Z] chunk 362/366: days 5415..5430 (15 days) +[2026-09-30T14:29:05Z] chunk 363/366: days 5430..5445 (15 days) +[2026-09-30T14:29:12Z] chunk 364/366: days 5445..5460 (15 days) +[2026-09-30T14:29:18Z] chunk 365/366: days 5460..5475 (15 days) +[2026-09-30T14:29:21Z] chunk 366/366: days 5475..5479 (4 days) +[2026-09-30T14:29:58Z] gauges with finite NSE: 2365 / 2365 +[2026-09-30T14:29:58Z] median NSE (finite only): 0.7741 +[2026-09-30T14:29:58Z] median KGE (finite only): 0.7970 +[2026-09-30T14:29:58Z] baseline → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T13-50-59Z-train-and-test/baseline +run complete → /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-30T13-50-59Z-train-and-test diff --git a/experiments/levers/replay_s43_trainyears.yaml b/experiments/levers/replay_s43_trainyears.yaml new file mode 100644 index 00000000..c15b9e1a --- /dev/null +++ b/experiments/levers/replay_s43_trainyears.yaml @@ -0,0 +1,332 @@ +# levers_replay_trainyears_s43: zero-step replay of 2026-09-27T10-31-30Z-train-and-test (checkpoint epoch_50_mb_9) +# scored on 1981/10/01..1996/09/30 (training years + WY1996 overlap). Generated by +# experiments/levers/make_replay_configs.py; everything below is the run's snapshot except those two edits. +# dam_release_full_off +# +# Full-population no-dam arm for the learned dam release: every reach routed as a +# channel. Matched arm: dam_release_full_learned.yaml (same seed, same config, plus +# the learned release at all 1,024 NID dam COMIDs). The pair measures the per-gauge +# change the learned release makes over the whole eval population, at gauges below a +# NID dam >= 10 MCM and at gauges without one. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_off.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 43 +np_seed: 43 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + checkpoint: /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T10-31-30Z-train-and-test/checkpoints/epoch_50_mb_9 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1981/10/01 + end_time: 1996/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/replay_trainyears_s42.yaml b/experiments/levers/replay_trainyears_s42.yaml new file mode 100644 index 00000000..a5cb48b4 --- /dev/null +++ b/experiments/levers/replay_trainyears_s42.yaml @@ -0,0 +1,332 @@ +# levers_replay_trainyears_s42: zero-step replay of 2026-09-27T07-29-47Z-train-and-test (checkpoint epoch_50_mb_9) +# scored on 1981/10/01..1996/09/30 (training years + WY1996 overlap). Generated by +# experiments/levers/make_replay_configs.py; everything below is the run's snapshot except those two edits. +# dam_release_full_off +# +# Full-population no-dam arm for the learned dam release: every reach routed as a +# channel. Matched arm: dam_release_full_learned.yaml (same seed, same config, plus +# the learned release at all 1,024 NID dam COMIDs). The pair measures the per-gauge +# change the learned release makes over the whole eval population, at gauges below a +# NID dam >= 10 MCM and at gauges without one. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_off.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 42 +np_seed: 42 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + checkpoint: /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T07-29-47Z-train-and-test/checkpoints/epoch_50_mb_9 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1981/10/01 + end_time: 1996/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/replay_trainyears_s43.yaml b/experiments/levers/replay_trainyears_s43.yaml new file mode 100644 index 00000000..c15b9e1a --- /dev/null +++ b/experiments/levers/replay_trainyears_s43.yaml @@ -0,0 +1,332 @@ +# levers_replay_trainyears_s43: zero-step replay of 2026-09-27T10-31-30Z-train-and-test (checkpoint epoch_50_mb_9) +# scored on 1981/10/01..1996/09/30 (training years + WY1996 overlap). Generated by +# experiments/levers/make_replay_configs.py; everything below is the run's snapshot except those two edits. +# dam_release_full_off +# +# Full-population no-dam arm for the learned dam release: every reach routed as a +# channel. Matched arm: dam_release_full_learned.yaml (same seed, same config, plus +# the learned release at all 1,024 NID dam COMIDs). The pair measures the per-gauge +# change the learned release makes over the whole eval population, at gauges below a +# NID dam >= 10 MCM and at gauges without one. +# +# The sr_n0_gamma recipe verbatim (run 2026-09-12T23-39-03Z, config snapshot copied +# unchanged below this header): full gages_3000 population (2,365 gauges after +# the filters), train 1981-10-01..1995-09-30, test 1995-10-01..2010-09-30, seed 42. +# Compare only with its matched arm, on the same gauge CSV (trap T19). +# +# Run from the repo root, CPU (trap T13), main workspace: +# /target/release/ddrs --config config/experiments/dam_release_full_off.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs run --workflow train-and-test --backend cpu + +# sr_n0_gamma +# +# The KAN emits ONLY n_0 and gamma. p_spatial is fixed at 21 and q_spatial at +# 0.65 (params.defaults), so the channel shape is prescribed and the head +# carries the two roughness parameters of n(d) = n_0 * (d/d_ref)^(-gamma). +# +# Why (2026-09-12, findings §36.5-36.7): with p and q free, a learned gamma +# rode the width channel (rho(p, gamma) 0.89, rho(q, gamma) 0.76) and the +# head traded q against gamma, so the sweep could not tell degeneracy from +# unidentifiability. Fixing p and q removes that direction by construction, +# and a two-output head is what the loss-landscape and adjoint instruments +# can probe over (n_0, gamma) directly. +# +# Matched control: sr_n0_only.yaml (n_0 alone, same fixed p and q, gamma = 0). +# +# ddrs --config config/experiments/sr_n0_gamma.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test --plot --backend cpu + +# head_shared_linear +# +# One arm of the head-topology experiment. Derived from the 500-update +# nse-batch baseline 2026-09-10T21-21-48Z-conus-train-and-test +# (NSE 0.7376 / KGE 0.7600) by changing the kan_head section and nothing +# else. Screen that motivated these arms: +# .ddrs/experiments/head-arch/2026-09-11-screen +# experiments/head_arch/analyze.py +# +# MATCHED CONTROL. Current architecture: one shared trunk, Linear(H,P) +# read-out. Measured trunk effective rank 4.36 of 21; init affine R^2 between +# logit(n) and logit(q) at the chance level for that rank. +# +# Run (note --workspace: without it .ddrs lands beside this config): +# ddrs --config config/experiments/head_shared_linear.yaml \ +# --workspace /home/tbindas/projects/ddrs/.ddrs \ +# run --workflow train-and-test + +# ddrs MERIT training config. +# +# Hyperparameters are pulled verbatim from +# ~/projects/ddr/config/merit_training_config.yaml. +# Forcing is daily; the loader interpolates to hourly (repeat × 24, trim to +# n_hourly) before feeding the Muskingum-Cunge engine — same pattern as +# DDR's StreamflowReader. + +mode: training +workflow: train-and-test # ddrs plan/run picks this up; override with --workflow X +geodataset: merit +seed: 43 +np_seed: 43 + +# Source paths — read in place by ddrs's Rust loaders (zarrs + icechunk + netcdf). +# No export/materialization step; the harness reads DDR's live data sources +# straight from disk. +data_sources: + # CONUS MERIT inputs as available on the workstation. + attributes: /home/tbindas/projects/ddr/data/merit_global_attributes_v2.nc + conus_adjacency: /home/tbindas/projects/ddr/data/merit_conus_adjacency.zarr + gages_adjacency: /home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr + # Icechunk dHBV2 UH retrospective (divide-major, daily m³/s) + streamflow: /mnt/ssd1/data/icechunk/merit_dhbv2_UH_retrospective.ic + # Icechunk USGS daily observations + observations: /mnt/ssd1/data/icechunk/usgs_daily_observations + gages: /home/tbindas/projects/ddr/references/gage_info/gages_3000.csv + +experiment: + batch_size: 64 + start_time: 1981/10/01 + end_time: 1995/09/30 + checkpoint: /home/tbindas/projects/ddrs/.ddrs/runs/2026-09-27T10-31-30Z-train-and-test/checkpoints/epoch_50_mb_9 + epochs: 50 # 2026-09-10: 30 -> 50, with grad_accum_steps 4 this is 500 optimizer updates (was 60) + rho: 90 + shuffle: true + warmup: 5 + # Gradient accumulation ON. `batch_size` is now the MICRO-batch; the optimizer + # steps once per `grad_accum_steps` micro-batches, so the effective batch is + # 64 * 4 = 256 gauges and the gradient is averaged over ~4x more basins. + # + # THE ARITHMETIC — read this before trusting a run: + # 1,841 gauges / 64 = 28 full + 1 partial = 29 micro-batches per epoch + # (accumulation sets `drop_last = false`, driver.rs:268-275, so the + # 49-gauge tail is KEPT — unlike the non-accumulating path) + # 29 / 20 = one group of 20 + one partial group of 9. + # A partial group STILL STEPS: only `micros_drawn == 0` breaks the loop + # (driver.rs:415), and the 1/Σn renormalization makes unequal group sizes + # exact. So this is 2 optimizer steps per epoch, not 1. + # x 30 epochs = 60 TOTAL UPDATES + # + # Effective batch is 20*64 = 1,280 of 1,841 gauges — 70% of the training set + # in a single gradient, i.e. near full-batch. Very low variance, very few steps. + # + # THAT IS THE RISK. Calibration: the run that beat the summed-Q' baseline + # (2026-07-30T00-24, median NSE 0.6799) took 180 updates at lr 1e-3 and moved + # the head L2(dw) = 9.75. The 2026-08-04 run that hit NSE 0.6782 took 280. + # Adam's per-weight displacement is bounded by ~lr per step, so the budget + # here is 40*0.001 + 20*0.0005 = 0.050 — about 38% of the 0.132 that the + # 180-step run realized. Expect under-training. + # + # If it under-trains, prefer more STEPS over a hotter lr: + # * `grad_accum_steps: 20` -> 7 (5 steps/epoch, 150 updates, eff batch 448) + # * `grad_accum_steps: 20` -> 4 (8 steps/epoch, 240 updates, eff batch 256) + # Wall clock is forward-pass bound (~27.5 s/micro-batch), so all of these cost + # the SAME time — 29 forwards per epoch regardless. Lowering accum_steps buys + # optimizer steps for free; it only trades gradient variance for step count. + # + # The historical `grad_accum_steps: 37` was WORSE than either: with 28-37 + # micro-batches per epoch it collapsed a whole epoch into ONE update, so 50 + # epochs bought 50 updates for 1,850 forward passes. Wall clock here is + # forward-pass bound (~27.5 s/micro-batch), not step bound, so accumulating + # costs nothing in time — it only trades update count for gradient quality. + # + # ALWAYS verify the head actually moved (L2(dw)) before believing a result: + # the 2026-07-31 AdaDelta run reported plausible losses while L2(dw) = 1.9e-4, + # i.e. it never left initialization. + use_grad_accum: true + # 2026-09-10: 20 -> 4. The 2026-09-08 run made 60 optimizer updates in total + # (37 micro-batches/epoch at 2,365 kept gauges, 2 steps/epoch, 30 epochs) and + # findings §21 shows it never reached a stationary point: on BOTH the training + # and testing windows the loss still falls if roughness rises, at ~79% of + # gauges (trimmed gradient alignment 0.96/0.94). At accum 4 that is 10 + # steps/epoch, so 500 updates over 50 epochs, effective batch 4*64 = 256. + # Wall clock is forward-pass bound at 37 forwards/epoch either way: the + # 30-epoch run trained in 64 min, so 50 epochs is ~107 min. The "27.5 s per + # micro-batch" figure above is stale; measured here it is ~3.5 s. + grad_accum_steps: 4 + # Adam, reverting the 2026-07-31 AdaDelta run. That run's head moved + # L2(dw) = 1.9e-4 (0.0004% of ||w||) across all 30 epochs — it never left + # initialization. Cause: `experiment.learning_rate` IS applied to AdaDelta + # (driver.rs:404 passes lr to optimizer.step; adadelta.rs:123 multiplies + # delta by it) despite config.rs:211-213 documenting the key as inert, so + # every update ran 1000x scaled down. dHBV intends AdaDelta at lr=1.0. + optimizer: adam + loss: + kind: nse-batch + # Step schedule. Keys are the FIRST epoch at which the lr applies + # (`resolve_lr` takes the largest key <= epoch, 1-indexed). At 2 optimizer + # steps per epoch (see the accumulation note above): + # epochs 1-20 @ 0.001 (40 updates) + # epochs 21-30 @ 0.0005 (20 updates) + # Total displacement budget ~0.050. The decay lands 2/3 of the way through, + # which is the right shape — the concern is the step COUNT, not the schedule. + # + # Watch `n_at_floor` in the log: the lr=0.01 run pinned 47.3% of CONUS at the + # 0.015 floor, and both 2026-08-04 runs collapsed from 0% to 25-36% between + # epochs 3 and 5 at lr 2e-3. Starting at 1e-3 is deliberately cooler than that. + # Near-full-batch gradients (1,280 gauges) should also damp the collapse, since + # it looked like a high-variance small-batch effect — but that is a hypothesis, + # not an established result. If `n_at_floor` climbs past a few percent, the lr + # is still too hot. + # 2026-09-10: flattened. n froze at epoch 10 in the previous run, which is + # exactly where the old schedule cut 0.005 -> 0.001, so "the gradient + # vanished" and "the steps became too small" were not separable from the + # trajectory (§10 correction). §21 shows the gradient had NOT vanished. + # Keys are the FIRST epoch at which the lr applies. At 10 steps/epoch: + # epochs 1-20 @ 0.005 (200 updates) + # epochs 21-40 @ 0.002 (200 updates) + # epochs 41-50 @ 0.001 (100 updates) + # Displacement budget ~1.3 against the previous ~0.13. + # WATCH `n_at_floor`: lr 0.01 pinned 47.3% of CONUS at the 0.015 floor and + # lr 2e-3 collapsed 0% -> 25-36% by epoch 5 in earlier small-batch runs. + # Holding 0.005 for 200 updates at an effective batch of 256 is the real risk + # in this plan. If n_at_floor passes a few percent, drop the first rate to + # 0.002 and keep the step count. + learning_rate: + 1: 0.005 + 21: 0.002 + 41: 0.001 + grad_clip_max_norm: 1.0 + +kan_head: + hidden_size: 21 + num_hidden_layers: 2 + # B-spline grid intervals (`num` in pykan). Matches DDR's + # ~/projects/ddr/config/merit_training_config.yaml::kan.grid. + grid: 50 + # B-spline order. Matches DDR's ::kan.k. (pykan's KANLayer default is 3, + # but DDR overrides to 2 for production.) + k: 2 + input_var_names: + - SoilGrids1km_clay + - aridity + - meanelevation + - meanP + - NDVI + - meanslope + - log10_uparea + - SoilGrids1km_sand + - ETPOT_Hargr + - Porosity + # All three learned together. The baseline fixed p_spatial at 21 + # (docs/2026-09-08-fixed-p-assessment.md); §30 argued that learning p and q + # as a pair relocates the free parameter onto something falsifiable against + # remotely sensed river width, and §31 showed q alone was only n amplified + # fourfold. Because this differs from the baseline in TWO ways (learnable set + # and head topology), the shared-linear arm is the matched control: read + # these arms against each other, never against 0.7376/0.7600. + learnable_parameters: + - n + - gamma + # Precip-conditioned mass-preserving daily->hourly disaggregation head. + # The within-day shape is driven by the hourly AORC precip window + # [d-1,d,d+1] (+ daily-Q taps + static attrs); the daily mean is conserved + # exactly. Loss stays the historical L1 (no experiment.loss block). + # Warm-started FROZEN from the standalone capacity pretrain (chunk_days=1); + # architecture fields must match that checkpoint or load_record fails. + # `enabled: false` makes this whole block inert — the loader uses flat + # repeat-24 (nearest) upsampling instead. Flip to true for the head-on + # arm of the ablation; nothing else needs to change. + disaggregation: + enabled: false + hidden_size: 16 + num_hidden_layers: 2 + grid: 20 + k: 3 + boundary_blend: 0.0 + chunk_days: 1 + pretrained_checkpoint: /home/tbindas/projects/ddrs/output/disagg_pretrain/capacity_chunk1.mpk + freeze: true + +# Routing-engine knobs — DDR's MERIT defaults (matches mock_config in tests). +params: + parameter_ranges: + # Stage-roughness exponent. Non-negative because channels get SMOOTHER as + # they fill; capped at 0.5 because the depth exponent is 3/(5+3q+3*gamma) + # and roughness grows without bound as depth goes to zero. 0.183 reproduces + # observed at-a-station hydraulic geometry when paired with q ~ 0.65, and + # the sigmoid centre of this box is 0.25. + gamma: [0.0, 0.5] + n: [0.015, 0.25] + q_spatial: [0.0, 1.0] + p_spatial: [1.0, 200.0] + attribute_minimums: + discharge: 1.0e-4 + slope: 1.0e-3 + velocity: 0.01 + depth: 0.01 + bottom_width: 0.01 + defaults: + p_spatial: 21.0 + # Fixed width exponent: the at-a-station Leopold & Maddock value b/f = 0.65 + # (design doc §2.2), which with gamma ~ 0.18 reproduces the observed + # at-a-station exponents. Must lie inside parameter_ranges.q_spatial. + q_spatial: 0.65 + log_space_parameters: + - p_spatial + # Corrected physics instead of DDR's formulation. With `false` the routing + # core uses: + # * exact trapezoidal celerity c = v·β, β = 5/3 − (4/3)·A·√(1+z²)/(T·P) + # instead of the wide-rectangular 5/3 limit (which is 22-27% high for the + # channels this code builds — κ = b/y ≈ 0.7-1.8, so β ≈ 1.31-1.36), and + # * Cunge-derived Muskingum X = clamp(0.5(1 − Q/(T·S·c·L)), 0, 0.5) instead + # of the constant 0.3, which was injecting a median 28× excess numerical + # diffusion. + # Negative-discharge counting before the S28 clamp is reported in BOTH modes. + # This breaks `compare_ddr_sandbox`'s ABSOLUTE MATCH by design — that example + # must be run with the default `ddr_match: true`. + # See `.claude/PHYSICS-CORRECTIONS.md`. + ddr_match: false + # Provably zero negative discharges. Clamps the Muskingum INPUTS (K, X) into + # the non-negative-coefficient window `2X <= Cr <= 2(1-X)`, Cr = dt/K: + # K <- max(K, dt*(1+d)/2) => Cr <= 2/(1+d) + # X <- min(X_cunge, (1-d)*Cr/2, (1-d)*(1-Cr/2)) + # with d = POSITIVITY_DELTA = 1e-2. Clamping K and X (not the coefficients) + # keeps `c1+c2+c3 = 1` exact, so mass is conserved; clamping c3 would not. + # With c1,c3 >= 0 and q_t,q' > 0, induction over the topological order of the + # forward substitution proves the whole solve is non-negative. + # + # Measured: 0 / 197,461,880 solves on 1,841 gauges (was 55,181, 0.0279%), + # replicated across two windows, two backends, and two trained heads. + # + # COST — this is not free. The X cap binds on 95.3% of reach-timesteps and + # median X falls 0.4976 -> 0.0794 (6.3x), so it largely REPLACES the Cunge X + # rather than shading it: numerical diffusion becomes stability-set instead of + # matched to hydraulic diffusivity. NSE/KGE impact is UNMEASURED, and this is + # the first training run to use the flag. Compare against a matched + # `enforce_positivity: false` control before trusting the result. + # + # Root-cause alternative: median Cr is 0.226, i.e. the typical MERIT reach is + # ~4.4x too LONG for an hourly step. Subdividing reaches ~4x brings Cr to ~1, + # where X_max -> 0.5 and the cap stops biting. See .claude/PHYSICS-CORRECTIONS.md. + # + # Requires ddr_match: false (rejected at load otherwise). + # OFF. It delivered provably zero negative solves (0 / 197,461,880), but the + # cure was worse than the disease: capping X collapsed the median from 0.4976 + # to 0.0794 on 95.3% of reach-timesteps, and because the cap is applied at + # RUNTIME to a learned celerity it made X ~ Cr ~ 1/n — handing the optimizer a + # lever it rode straight to the roughness floor. Same lr, flag on vs off: + # n_at_floor 35% -> 97.9%. Superseded by reach subdivision (a BUILD-TIME fix + # with no gradient path): see docs/superpowers/plans/2026-08-05-reach-subdivision.md + enforce_positivity: false + # cuSPARSE triangular solve. Pair with `ddrs run --backend cuda`; it is inert + # under `--backend cpu`. This is a DIFFERENT `Backward` impl from the host + # solve, sitting directly downstream of the ddr_match/enforce_positivity + # terms — gradient-checked on GPU by `tests/cuda_backward_parity.rs`. + sparse_solver: cuda + # MUST be false when ddr_match is false: the fused CUDA-graph kernel + # (cuda_graph/geometry_kernel.rs) hardcodes DDR's 5/3 celerity, so a captured + # graph would replay the DDR forward while the backward used the corrected + # chain rule — a silent gradient mismatch. Config load rejects the pair. + use_cuda_graphs: false + +# Test-mode overlay. When the binary is run with --mode testing, these keys +# replace the corresponding ones in `experiment:`. Absent keys inherit. +# +# IMPORTANT: batch_size SEMANTIC SHIFTS between modes: +# - experiment.batch_size (training) = number of GAUGES per mini-batch +# - testing.batch_size = number of DAYS per chunk +testing: + start_time: 1981/10/01 + end_time: 1996/09/30 + batch_size: 15 # DAYS, not gauges + rho: null # disabled in test mode diff --git a/experiments/levers/report.txt b/experiments/levers/report.txt new file mode 100644 index 00000000..f1e202d1 --- /dev/null +++ b/experiments/levers/report.txt @@ -0,0 +1,161 @@ +NON-DAM LEVERS ON THE 2,365-GAUGE POPULATION MEDIAN (Phase A, offline). 2026-09-30. + +Base: seed-42 no-dam run 2026-09-27T07-29-47Z, test WY1996-2010, median NSE 0.7391 / KGE 0.7591, 2,365 gauges. +Target +0.03 = 0.7691. "pop" = change of the population median vs 0.7391 with a 95 % gauge-bootstrap interval; "paired" = +median per-gauge change; "up" = share of gauges that improve. Every fitted quantity is fitted on training years +(WY1982-1995, or WY1986-1995 where stated) and scored on WY1996-2010. The gauge bootstrap does not include training-seed +noise: the two no-dam seeds differ by 0.005 at the median, so treat any single-seed recipe difference below ~0.005 as +unresolved. Scripts, outputs and configs: experiments/levers/ (pre-registration in prereg.txt, written before any +training-year output existed). + +BOTTOM LINE +- The runoff multiplier is not a lever. The model's volume is unbiased on the training years (pred/obs 1.004 at the + median gauge, seed 42; 1.001 seed 43); the +4.5 % test-period excess is a Q' drift that 1982-1995 cannot see. Honest + multipliers move the median by +0.0005 to +0.0008; the earlier +0.005 for a 0.95 scalar was chosen on test years. +- Head variance is real but small at the median: the seed gap (0.005) is gamma wandering; fixing gamma = 0 is worth + about +0.002 vs seed 42 (+0.004 vs the two-seed mean); a two-seed ensemble adds only +0.001 over its members' mean. + Training is near a skill plateau: epoch 40 -> 50 adds +0.0013 (interval includes 0) on fixed test years. +- The lever that moves the median is a multi-product Q' ensemble: route the working recipe on UH, dHBV2-distributed and + the daily CudaLSTM Q' and average with weights fitted on the training years: 0.7650, +0.0259 [+0.0221,+0.0309], + 75 % of gauges up, KGE +0.0043. Member choice was test-free (forward selection on training-year summed Q'). +- Together: ensemble +0.026, gamma = 0 (and optionally learned p, q) +0.001 to +0.005 inside the ensemble, dams +0.003 + gives +0.029 to +0.033. +0.03 is reachable only with the ensemble; without it the honest non-dam levers sum to + <= +0.007 and, with dams, reach about +0.01 at most. + +1. RUNOFF VOLUME (Q') MULTIPLIERS + +Training-year predictions: zero-step replays of each seed's last checkpoint (epoch_50_mb_9; the Phase-2 fallback in +src/cli/run.rs tests the resumed checkpoint when Phase 1 takes no step), testing window 1981-10-01..1996-09-30, binary +/home/tbindas/.claude/jobs/dacd6d8c/tmp/bin/ddrs-v6-77aa4a0, CPU, main workspace. Runs 2026-09-30T13-13-59Z (seed 42, +median NSE 0.7781 over the window) and 2026-09-30T13-50-59Z (seed 43, 0.7741). Configs replay_trainyears_s42.yaml, +replay_s43_trainyears.yaml. Validation on the WY1996 overlap (replay_check_*.txt): the replay differs from the source +run only by the source run's cold-start transient, median relative difference 7.7e-3 on 1995-10-02, 1.2e-4 in +January, 8.8e-8 in September; Jan-Sep 1996 volume ratio replay/source median 1.00000; same for seed 43. + +Why a multiplier cannot work (volume_s42.txt, volume_by_year_*.txt): predicted/observed volume at the median gauge is +1.004 on WY1982-1995 (51.8 % of gauges over) against 1.046 on WY1996-2010 (64.9 % over); seed 43 1.001 / 1.042. Routing +conserves mass, so this is the Q' product. By water year the bias tracks wetness and drifts upward after 2000: dry +years over-predicted (1988 1.02, 1992 1.06, 2000-2002 1.08 to 1.10 at 0.69-0.74 of normal flow), wet years +under-predicted (1983-84 0.97, 1996 1.00), 2003-2010 still 1.03-1.08 in near-normal years. The Q' model (and the +routing head) were fitted on roughly the training years, so their training-year volume is calibrated by construction +(hypothesis for UH; verified for the daily CudaLSTM, trained 1980-10..1995-09). Per-gauge scalars transfer only partly +between periods (Spearman train vs test 0.55). + +Results, seed 42 | seed 43 (NSE pop change; KGE pop change): + global scalar = median per-gauge LS k* (0.9964 | 0.9982), pre-registered +0.0005 [-0.0006,+0.0016] up 63 % | +0.0005; KGE +0.0004 | +0.0006 + global scalar maximizing training median NSE (0.96 | 1.0025) +0.0026 | -0.0005 (the choice flips between seeds: noise) + per-HUC2 median k*, pre-registered +0.0006 [-0.0008,+0.0030] up 52 % | +0.0008; KGE +0.0002 | +0.0005 + attribute-conditioned (ridge / GBM on log k*, 17 catchment attributes incl. the 10 head inputs, aridity, snow; + 10-fold by gauge | leave-one-HUC2-out) out-of-fold R2 0.015 / -0.047 (seed 42); NSE -0.0008 to +0.0005 (10-fold), + -0.0015 to -0.0039 (by HUC2); KGE down to -0.009 + state-dependent (volume_state_*.txt): power law, trailing-year state training years pick b = 1 and c = 0.1 (seed 42: NSE -0.0031, KGE +0.0042) + or no correction at all (seed 43); global quantile mapping -0.047 + per-gauge LS k* on training years (gauged calibration only) +0.0078 [+0.0042,+0.0123] up 59 % | +0.0094; KGE +0.0116 | +0.0119 + per-gauge quantile mapping on training years (gauged only) NSE -0.0038 | -0.0034, KGE +0.0425 | +0.0405 + per-gauge k* fitted ON TEST YEARS (in-sample oracle, not a lever) +0.0286 | +0.0289; KGE +0.0406 | +0.0391 + reference: fixed 0.95 (chosen on test years earlier) +0.0047 | +0.0029; KGE -0.0049 | -0.0071 +Extra lever, daily timing blend p' = (1-|w|) p(t) + |w| p(t -/+ 1) (timing_*.txt): training years pick w = 0 globally +(no change, both seeds); per-HUC2 +0.0009 | +0.0004; attribute GBM +0.0014 | +0.0021 (KGE -0.0025); per-gauge w (gauged) ++0.0082 | +0.0102; per-gauge (w, k) jointly (gauged) +0.0160 | +0.0193, KGE +0.0124. + +2. ROUTING-HEAD CONVERGENCE AND VARIANCE (variance.txt, variance_gamma.txt, loss_robust.txt, convergence_ckpt.txt) + +Convergence. The run.log loss is flat from about epoch 5-10: seed 42 slope over epochs 41-50 +0.0019 +- 0.0035 per +epoch at a level of 0.298 (gauge-weighted mean of micro-batch losses); robust per-epoch median, Theil-Sen +0.0024 +[-0.0068,+0.0083] per epoch. The micro-batch noise (random gauges and 90-day windows) is about +-12 % of the level over +ten epochs, so the log cannot certify convergence at the precision that matters. The parameters are not converged: +seed-42 median gamma 0.233 (epoch 10) -> 0.151 (25) -> 0.067 (50), median n 0.086 -> 0.074 -> 0.058, median reach +|ln n| change 0.18 from epoch 10 to 25 and 0.23 from 25 to 50. Direct test on fixed data (seed-42 checkpoints +epoch_25_mb_9 and epoch_40_mb_9 replayed on WY1996-2000 with the source run's cold-start date, runs +2026-09-30T15-22-40Z and 2026-09-30T15-35-31Z; epoch 50 read from the source run): median NSE 0.7445 (epoch 25) -> +0.7462 (40) -> 0.7474 (50); 25 -> 50 +0.0030 [+0.0006,+0.0053], 40 -> 50 +0.0013 [-0.0006,+0.0028], paired +0.0002, +57 % up; KGE 0.7528 -> 0.7547 -> 0.7557. Test skill is still creeping up by about +0.001 per 10 epochs at lr 0.001, and +it moves with gamma falling (0.15 -> 0.07 over the same epochs), which the constant-gamma sweep says is the direction +that helps. Verdict: not converged in parameters; in skill, near a plateau, with about +0.001 to +0.003 left for longer +training on this seed, and it depends on which way gamma drifts (seed 43 ended at 0.27, worse). The epoch-10 checkpoint +was not replayed (dropped for time). + +Seed variance is gamma. Seeds 42 / 43: NSE 0.7391 / 0.7341, KGE 0.7591 / 0.7588. Basin-median gamma 0.071 / 0.273, +higher in seed 43 at 93 % of gauges; reach-level Spearman between the seeds' gamma fields -0.12 (n: 0.49). The per-gauge +seed loss grows with the gamma gap (tercile medians -0.0003 / -0.0007 / -0.0017) and with area (-0.0025 above +10,000 km2, 64 % down). The constant-gamma sweep (n, p, q learned, seed 42) gives 0.7453 / 0.7455 / 0.7416 / 0.7364 at +gamma 0 / 0.1 / 0.183 / 0.35, which predicts about -0.006 for the seeds' gamma gap, the size of the observed gap. + +Recipe (single seed each): + n only (gamma fixed at 0, channel fixed as now), 2026-09-12T23-39-06Z 0.7409, +0.0018 [-0.0005,+0.0049] up 58 %; KGE +0.0021 + gamma = 0 with p and q learned, 2026-09-12T03-53-34Z 0.7453, +0.0062 [+0.0035,+0.0103] up 57 %; KGE +0.0032 +Seed-42 runs draw identical gauge batches and 90-day windows, so their training losses compare pairwise: on identical +micro-batches the n-only head is 0.2-0.7 % lower than the working n0 + gamma head, and the gamma = 0, n+p+q head 1.2-1.5 % +lower at 72-75 % of micro-batches in every epoch block. Adding gamma to the n-only head makes the training fit worse: +an optimization problem along an unidentified direction, not missing capacity. + +Near-median gauges (174 within 0.03 below 0.7391): smaller (534 vs 872 km2), shorter longest flow path (60 vs 80 km; +0.69 vs 0.92 d at a nominal 1 m/s), more GAGES-II Ref (31 % vs 21 %), fewer NID dams >= 10 MCM (25 % vs 39 %). Seed 43 +costs them -0.0006 at the median (58 % down); the quarter that lose most (44) are low-snow (snow fraction 0.04 vs 0.09), +609 km2, 0.86 d, in HUC2 02, 07 and 03. At the median line seed 43 drops 54 gauges below it and lifts 18 above. Across +the population the size of the seed difference rises with travel time (Spearman +0.26) and area (+0.17) and falls with +snow fraction (-0.37). + +Ensembles of heads (equal weights): two seeds 0.7376 (-0.0015 vs seed 42, +0.0010 over the members' mean 0.7366, KGE ++0.0016); all ten UH-store 50-epoch nse-batch variants 0.7436 (+0.0045, +0.0035 over their mean). Head variance +reduction is worth about +0.001 to +0.0035. + +2b. MULTI-PRODUCT Q' ENSEMBLE (store_selection.txt, ensemble_weights_3member.txt, ens_detail.txt, combine_ens.txt) + +The working recipe (seed-42 config, only the Q' store changed) was trained on five other stores on 2026-09-13. Member +selection was test-free: greedy forward selection on TRAINING-year (WY1986-1995) summed-Q' baselines, from UH, adds +dHBV2-distributed AORC (0.7529 -> 0.7608) then the daily CudaLSTM (-> 0.7695) and stops; the dHBV2-lumped and HydroDL +stores are rejected (the hourly LSTM was not a candidate: its baseline was too heavy to compute here). All products are +out of sample in time on WY1996-2010. Weights were fitted on routed training-year replays (2026-09-30T14-30-08Z LSTM, +2026-09-30T14-56-59Z DIST, WY1986-1995). + equal weights UH + DIST + LSTM 0.7637, +0.0246 [+0.0207,+0.0302] up 69 %; KGE -0.0001 + training-year weights UH 0.50, DIST 0.25, LSTM 0.25 0.7650, +0.0259 [+0.0221,+0.0309] up 75 %; KGE 0.7634 (+0.0043) + per-HUC2 training-year weights +0.0247; KGE +0.0038 + training-year weights UH 0.70, LSTM 0.30 (two stores) 0.7637, +0.0246 [+0.0206,+0.0292] up 71 %; KGE +0.0012 + per-gauge NNLS weights on training years (gauged only) 0.7754, +0.0363; KGE +0.0177 + unselected: all six stores, equal weights 0.7338, -0.0053; KGE -0.0760 +The gain is also present in-sample (training-year median 0.8043 for the weighted ensemble vs 0.7861 for UH). +Mechanism: the LSTM's errors are weakly correlated with UH's (median residual correlation 0.62, against 0.93 for DIST and +0.997 between the UH seeds); r rises 0.880 -> 0.892; the test-period volume biases partly cancel (beta 1.045 UH, 1.065 +DIST, 0.965 LSTM, 1.024 equal-weight ensemble); alpha falls 0.914 -> 0.869, which NSE rewards and KGE does not. + +3. COMBINED, AND WHETHER +0.03 IS REACHABLE (vs 0.7391; single seeds where noted) + (a) best honest runoff multiplier (global or per-HUC2) +0.0005 to +0.0008 (both seeds) + per-gauge calibration, gauged sites only +0.008 to +0.009 + (b) variance / convergence: gamma = 0 (n only) +0.0018 (single seed; +0.004 vs the two-seed mean) + gamma = 0 with p, q learned +0.0062 (single seed) + two-seed head ensemble +0.001 over the members' mean + multi-product Q' ensemble, training-year weights +0.0259 (KGE +0.0043) + (c) dam recipe (per-dam release, research/findings/2026-09-28-per-dam-release-calibration-findings.md) about +0.003 + Stacked on per-gauge predictions, equal weights (combine_ens.txt): n-only + DIST + LSTM 0.7647 (+0.0256); + gamma0 n+p+q + DIST + LSTM 0.7683 (+0.0292, KGE +0.0008); gamma0 + n-only + DIST + LSTM 0.7688 (+0.0297 [+0.0257, + +0.0347], 76 % up, KGE +0.0051). Adding the dam recipe's +0.003 to the ensemble alone or to the stacked arms gives +0.029 to +0.033 (0.768 to 0.772). The + recipe part inside the ensemble (+0.001 to +0.005) is within the seed noise; the ensemble part is five times it. + Verdict: +0.03 is reachable only with the multi-product ensemble; it is at the margin, and its KGE gain is small. + +4. WHAT TO IMPLEMENT FIRST +First: multi-product Q' forcing. Cheapest form (no engine change): train the working recipe on UH, DIST and daily-LSTM +(already done for seed 42) and average the routed test outputs with the training-year weights (0.50 / 0.25 / 0.25). +Engine form: let data_sources.streamflow take a weighted list of stores and average Q' per divide and day at read time +(average only over stores that cover the divide: the LSTM store has 288,421 divides vs 197,088, and absent divides are +filled with 0.001 today, which would dilute the mean), then train one routing head jointly on the blended Q'. Expected ++0.02 to +0.026 median NSE, KGE about +0.004. Risks: routing is mildly nonlinear in Q (depth, gamma), so input blending +is not identical to output averaging and must be measured with one training run against the output ensemble; part of +the test gain is the members' opposite volume drifts cancelling in 1996-2010 (the training years support the weights, +but the cancellation may not hold in other periods); smoother hydrographs (alpha 0.87) mean KGE barely moves; three +stores must exist for any new domain or period (not for the global runs today); each store's head learns a different n, +so an output ensemble has no single parameter field for the paper, whereas the input blend keeps one head. +Second, together with the first: fix gamma = 0 (learnable_parameters: [n], config only). About +0.002 and it removes the +seed-to-seed drift; a narrow gamma box ([0, 0.1]) or a prior toward 0 is the alternative if gamma must stay. Learning p +and q as well adds about +0.004 more (single seed) but conflicts with the fixed-channel design chosen for +identifiability (q is flat at 85 % of well-fit gauges), so that is the user's call. +Do not implement a learned per-reach runoff multiplier: trained on 1982-1995 it will learn about 1 (the honest global +scalar is worth +0.0005) and, being free per reach, it would absorb timing and routing errors (the review's warning) with +no test-period benefit to show for it. Compare any future volume term against the plain scalar null (+0.0005) and the +ensemble, not against 0.95. Longer training or a flatter lr schedule alone: about +0.001 per 10 epochs on seed 42 and +riding on gamma's drift, so fix gamma first and only then judge whether more epochs pay. + +Queue/infra notes: all replays ran one at a time, detached, on CPU with RAYON_NUM_THREADS=8 and DDRS_JOURNAL_OFF=1; +`ddrs run`/`plan` relocked .ddrs/sources.lock several times (streamflow, reservoirs drift), as every run in the shared +workspace does. No Rust code was edited; nothing was committed. One caveat in variance.txt: a single exploding +micro-batch loss (seed 42, epochs 1-10) dominates that block's mean paired difference; read the median column there. diff --git a/experiments/levers/rerun.err b/experiments/levers/rerun.err new file mode 100644 index 00000000..a03613e9 --- /dev/null +++ b/experiments/levers/rerun.err @@ -0,0 +1,8 @@ +/home/tbindas/projects/ddrs/.claude/worktrees/agent-a92e512a7c47c97b4/experiments/levers/volume_by_year.py:22: RuntimeWarning: divide by zero encountered in divide + r = np.where(ok, np.where(m, P, 0).sum(1) / np.where(m, O, 0).sum(1), np.nan) +/home/tbindas/projects/ddrs/.claude/worktrees/agent-a92e512a7c47c97b4/experiments/levers/volume_by_year.py:22: RuntimeWarning: invalid value encountered in divide + r = np.where(ok, np.where(m, P, 0).sum(1) / np.where(m, O, 0).sum(1), np.nan) +/home/tbindas/projects/ddrs/.claude/worktrees/agent-a92e512a7c47c97b4/experiments/levers/volume_by_year.py:22: RuntimeWarning: divide by zero encountered in divide + r = np.where(ok, np.where(m, P, 0).sum(1) / np.where(m, O, 0).sum(1), np.nan) +/home/tbindas/projects/ddrs/.claude/worktrees/agent-a92e512a7c47c97b4/experiments/levers/volume_by_year.py:22: RuntimeWarning: invalid value encountered in divide + r = np.where(ok, np.where(m, P, 0).sum(1) / np.where(m, O, 0).sum(1), np.nan) diff --git a/experiments/levers/rerun.log b/experiments/levers/rerun.log new file mode 100644 index 00000000..090cb4e3 --- /dev/null +++ b/experiments/levers/rerun.log @@ -0,0 +1,31 @@ +2026-09-30T14:33:48Z start volume.py 42 2026-09-30T13-13-59Z-train-and-test +2026-09-30T14:35:32Z done volume.py 42 2026-09-30T13-13-59Z-train-and-test exit=0 +2026-09-30T14:35:32Z start volume.py 43 2026-09-30T13-50-59Z-train-and-test +2026-09-30T14:37:17Z done volume.py 43 2026-09-30T13-50-59Z-train-and-test exit=0 +2026-09-30T14:37:17Z start timing.py 42 2026-09-30T13-13-59Z-train-and-test +2026-09-30T14:37:47Z done timing.py 42 2026-09-30T13-13-59Z-train-and-test exit=0 +2026-09-30T14:37:47Z start timing.py 43 2026-09-30T13-50-59Z-train-and-test +2026-09-30T14:38:18Z done timing.py 43 2026-09-30T13-50-59Z-train-and-test exit=0 +2026-09-30T14:38:18Z start volume_state.py 42 2026-09-30T13-13-59Z-train-and-test +2026-09-30T14:39:47Z done volume_state.py 42 2026-09-30T13-13-59Z-train-and-test exit=0 +2026-09-30T14:39:47Z start volume_state.py 43 2026-09-30T13-50-59Z-train-and-test +2026-09-30T14:41:16Z done volume_state.py 43 2026-09-30T13-50-59Z-train-and-test exit=0 +2026-09-30T14:41:16Z start volume_by_year.py 2026-09-27T07-29-47Z-train-and-test 2026-09-30T13-13-59Z-train-and-test +2026-09-30T14:41:17Z done volume_by_year.py 2026-09-27T07-29-47Z-train-and-test 2026-09-30T13-13-59Z-train-and-test exit=0 +2026-09-30T14:41:17Z start volume_by_year.py 2026-09-27T10-31-30Z-train-and-test 2026-09-30T13-50-59Z-train-and-test +2026-09-30T14:41:18Z done volume_by_year.py 2026-09-27T10-31-30Z-train-and-test 2026-09-30T13-50-59Z-train-and-test exit=0 +2026-09-30T14:41:18Z start variance.py +2026-09-30T14:41:30Z done variance.py exit=0 +2026-09-30T14:41:30Z start variance_gamma.py +2026-09-30T14:41:36Z done variance_gamma.py exit=0 +2026-09-30T14:41:36Z start store_ensembles.py +2026-09-30T14:41:42Z done store_ensembles.py exit=0 +2026-09-30T14:41:42Z start ens_detail.py +2026-09-30T14:41:50Z done ens_detail.py exit=0 +2026-09-30T14:41:50Z start store_selection.py +2026-09-30T14:42:01Z done store_selection.py exit=0 +2026-09-30T14:42:01Z start combine_ens.py +2026-09-30T14:42:06Z done combine_ens.py exit=0 +2026-09-30T14:42:06Z start combine.py s42=2026-09-27T07-29-47Z-train-and-test:2026-09-30T13-13-59Z-train-and-test s43=2026-09-27T10-31-30Z-train-and-test:2026-09-30T13-50-59Z-train-and-test g0=2026-09-12T03-53-34Z-train-and-test:- nonly=2026-09-12T23-39-06Z-train-and-test:- +2026-09-30T14:42:21Z done combine.py s42=2026-09-27T07-29-47Z-train-and-test:2026-09-30T13-13-59Z-train-and-test s43=2026-09-27T10-31-30Z-train-and-test:2026-09-30T13-50-59Z-train-and-test g0=2026-09-12T03-53-34Z-train-and-test:- nonly=2026-09-12T23-39-06Z-train-and-test:- exit=0 +2026-09-30T14:42:21Z all done diff --git a/experiments/levers/rerun_all.sh b/experiments/levers/rerun_all.sh new file mode 100755 index 00000000..55a579f9 --- /dev/null +++ b/experiments/levers/rerun_all.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash +# Re-run every analysis with the final common.py (share up/down with a 1e-9 threshold). Outputs go to the scripts' own +# .txt files; this log records exit codes. +cd /home/tbindas/projects/ddrs/.claude/worktrees/agent-a92e512a7c47c97b4/experiments/levers +PY=/home/tbindas/projects/ddr/.venv/bin/python +export OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 MKL_NUM_THREADS=4 +run() { echo "$(date -u +%FT%TZ) start $*" >> rerun.log; "$PY" "$@" > /dev/null 2>> rerun.err; echo "$(date -u +%FT%TZ) done $* exit=$?" >> rerun.log; } +run volume.py 42 2026-09-30T13-13-59Z-train-and-test +run volume.py 43 2026-09-30T13-50-59Z-train-and-test +run timing.py 42 2026-09-30T13-13-59Z-train-and-test +run timing.py 43 2026-09-30T13-50-59Z-train-and-test +run volume_state.py 42 2026-09-30T13-13-59Z-train-and-test +run volume_state.py 43 2026-09-30T13-50-59Z-train-and-test +run volume_by_year.py 2026-09-27T07-29-47Z-train-and-test 2026-09-30T13-13-59Z-train-and-test +run volume_by_year.py 2026-09-27T10-31-30Z-train-and-test 2026-09-30T13-50-59Z-train-and-test +run variance.py +run variance_gamma.py +run store_ensembles.py +run ens_detail.py +run store_selection.py +run combine_ens.py +run combine.py s42=2026-09-27T07-29-47Z-train-and-test:2026-09-30T13-13-59Z-train-and-test s43=2026-09-27T10-31-30Z-train-and-test:2026-09-30T13-50-59Z-train-and-test g0=2026-09-12T03-53-34Z-train-and-test:- nonly=2026-09-12T23-39-06Z-train-and-test:- +echo "$(date -u +%FT%TZ) all done" >> rerun.log diff --git a/experiments/levers/store_ensembles.py b/experiments/levers/store_ensembles.py new file mode 100644 index 00000000..c4525ca0 --- /dev/null +++ b/experiments/levers/store_ensembles.py @@ -0,0 +1,37 @@ +"""Cross-store ensembles without selection: the working recipe (n + gamma, seed 42, same config) was trained on five +other Q' stores on 2026-09-13. Report UH paired with EACH of them (equal weights), and UH with all of them, so the +dHBV2-distributed pairing is not chosen on test skill alone. Test years WY1996-2010, vs seed 42 (0.7391 / 0.7591). +Writes store_ensembles.txt.""" +import numpy as np + +import common as C + +STORES = { + "dhbv2 distributed aorc2f": "2026-09-13T17-22-30Z-train-and-test", + "dhbv2 lumped aorc2f": "2026-09-13T13-56-50Z-train-and-test", + "daily lstm": "2026-09-13T13-55-03Z-train-and-test", + "hourly lstm": "2026-09-13T14-14-46Z-train-and-test", + "hydrodl lstm aorc2f": "2026-09-13T17-21-58Z-train-and-test", +} +out = [] +ids, tt, P42, O = C.load_preds(C.S42) +B = C.metrics(P42, O) +bn, bk = B.nse.values, B.kge.values +P = {} +for name, r in STORES.items(): + i, t, Pr, _ = C.load_preds(r) + assert (t == tt).all() + P[name] = C.align(i, Pr, ids) + M = C.metrics(P[name], O) + out.append(f"single {name:28s} NSE {np.nanmedian(M.nse):.4f} KGE {np.nanmedian(M.kge):.4f}") +for name in STORES: + M = C.metrics((P42 + P[name]) / 2, O) + out.append(C.fmt(C.summarize(f"UH + {name}", M.nse.values, bn))) + out.append(C.fmt(C.summarize(f"UH + {name}", M.kge.values, bk, "KGE"))) +M = C.metrics(np.mean([P42] + list(P.values()), axis=0), O) +out.append(C.fmt(C.summarize("UH + all five stores (6 members)", M.nse.values, bn))) +out.append(C.fmt(C.summarize("UH + all five stores (6 members)", M.kge.values, bk, "KGE"))) +M = C.metrics(np.mean([P42, P["dhbv2 distributed aorc2f"], P["daily lstm"]], axis=0), O) +out.append(C.fmt(C.summarize("UH + dhbv2 distributed + daily lstm", M.nse.values, bn))) +open(C.HERE / "store_ensembles.txt", "w").write("\n".join(out) + "\n") +print("\n".join(out)) diff --git a/experiments/levers/store_ensembles.txt b/experiments/levers/store_ensembles.txt new file mode 100644 index 00000000..e5159c96 --- /dev/null +++ b/experiments/levers/store_ensembles.txt @@ -0,0 +1,18 @@ +single dhbv2 distributed aorc2f NSE 0.7300 KGE 0.7557 +single dhbv2 lumped aorc2f NSE 0.5822 KGE 0.5939 +single daily lstm NSE 0.6359 KGE 0.6579 +single hourly lstm NSE 0.5993 KGE 0.5152 +single hydrodl lstm aorc2f NSE 0.5689 KGE 0.5320 + UH + dhbv2 distributed aorc2f NSE median 0.7391 -> 0.7430 pop +0.0039 [+0.0008,+0.0086] paired +0.0044 up 61.0% down 39.0% + UH + dhbv2 distributed aorc2f KGE median 0.7591 -> 0.7631 pop +0.0040 [-0.0001,+0.0079] paired +0.0009 up 51.8% down 48.2% + UH + dhbv2 lumped aorc2f NSE median 0.7391 -> 0.7108 pop -0.0283 [-0.0332,-0.0216] paired -0.0264 up 29.7% down 70.3% + UH + dhbv2 lumped aorc2f KGE median 0.7591 -> 0.7203 pop -0.0388 [-0.0459,-0.0318] paired -0.0449 up 31.2% down 68.8% + UH + daily lstm NSE median 0.7391 -> 0.7525 pop +0.0134 [+0.0087,+0.0204] paired +0.0067 up 55.1% down 44.9% + UH + daily lstm KGE median 0.7591 -> 0.7395 pop -0.0196 [-0.0263,-0.0130] paired -0.0189 up 39.9% down 60.1% + UH + hourly lstm NSE median 0.7391 -> 0.7401 pop +0.0009 [-0.0040,+0.0077] paired -0.0043 up 47.3% down 52.7% + UH + hourly lstm KGE median 0.7591 -> 0.6708 pop -0.0883 [-0.0961,-0.0798] paired -0.0835 up 25.8% down 74.2% + UH + hydrodl lstm aorc2f NSE median 0.7391 -> 0.7170 pop -0.0221 [-0.0274,-0.0149] paired -0.0255 up 32.7% down 67.3% + UH + hydrodl lstm aorc2f KGE median 0.7591 -> 0.6951 pop -0.0640 [-0.0717,-0.0567] paired -0.0674 up 27.3% down 72.7% + UH + all five stores (6 members) NSE median 0.7391 -> 0.7338 pop -0.0053 [-0.0106,+0.0019] paired -0.0129 up 42.0% down 58.0% + UH + all five stores (6 members) KGE median 0.7591 -> 0.6831 pop -0.0760 [-0.0843,-0.0677] paired -0.0792 up 27.3% down 72.7% + UH + dhbv2 distributed + daily lstm NSE median 0.7391 -> 0.7637 pop +0.0246 [+0.0207,+0.0302] paired +0.0182 up 68.8% down 31.2% diff --git a/experiments/levers/store_selection.py b/experiments/levers/store_selection.py new file mode 100644 index 00000000..199e949f --- /dev/null +++ b/experiments/levers/store_selection.py @@ -0,0 +1,71 @@ +"""Test-free selection of Q' stores for an ensemble: greedy forward selection on the TRAINING years (WY1986-1995) using +the routing-free summed-Q' baselines, starting from UH; then the selected set scored on the test years, both as summed Q' +and as the routed working recipe (equal weights). 2,365 population gauges. Writes store_selection.txt.""" +import json +import pathlib + +import numpy as np +import pandas as pd + +import common as C + +BL = pathlib.Path("/home/tbindas/projects/ddrs/.ddrs/baselines") +TRAIN = {"UH": "abe830ef3781964b", "DIST": "196652afd33a6fda", "LSTM": "367784ef8974b46f", "LUMPED": "7b6fd0ba64be6ae2", + "HYDRODL": "8e6b5af4534def3c"} +ROUTED = {"UH": C.S42, "DIST": "2026-09-13T17-22-30Z-train-and-test", "LSTM": "2026-09-13T13-55-03Z-train-and-test", + "LUMPED": "2026-09-13T13-56-50Z-train-and-test", "HYDRODL": "2026-09-13T17-21-58Z-train-and-test"} +ids, tt, P42, O = C.load_preds(C.S42) + + +def load_bl(d): + m = json.load(open(d / "manifest.json")) + n, t = m["n_gauges"], m["n_days"] + P = np.fromfile(d / "predictions.f32", dtype="9s} {'test NSE':>9s}") +for keys in [["UH"], ["DIST"], ["LSTM"], ["LUMPED"], ["HYDRODL"], ["UH", "DIST"], ["UH", "LSTM"], ["UH", "LUMPED"], + ["UH", "HYDRODL"], ["UH", "DIST", "LSTM"], list(TRAIN)]: + say(f"{'+'.join(keys):28s} {med(TR, Otr, keys):9.4f} {med(TE, Ote, keys):9.4f}") +# greedy forward selection on training years +sel, best = ["UH"], med(TR, Otr, ["UH"]) +while True: + cands = [(med(TR, Otr, sel + [k]), k) for k in TRAIN if k not in sel] + if not cands: + break + v, k = max(cands) + if v <= best: + break + sel.append(k) + best = v + say(f" forward selection adds {k}: training median {v:.4f}") +say(f"selected on training years: {'+'.join(sel)} (training summed-Q' median {best:.4f})") +# the selected set, routed, on test years +R = {} +for k in sel: + i, t, P, _ = C.load_preds(ROUTED[k]) + R[k] = C.align(i, P, ids) +B = C.metrics(P42, O) +M = C.metrics(np.mean([R[k] for k in sel], axis=0), O) +say(C.fmt(C.summarize(f"routed, selected set {'+'.join(sel)}", M.nse.values, B.nse.values))) +say(C.fmt(C.summarize(f"routed, selected set {'+'.join(sel)}", M.kge.values, B.kge.values, "KGE"))) +open(C.HERE / "store_selection.txt", "w").write("\n".join(out) + "\n") +print("\n".join(out)) diff --git a/experiments/levers/store_selection.txt b/experiments/levers/store_selection.txt new file mode 100644 index 00000000..ee909b19 --- /dev/null +++ b/experiments/levers/store_selection.txt @@ -0,0 +1,18 @@ +summed Q' (routing-free), population 2,365 gauges; training WY1986-1995 1985-10-01..1995-09-30, test 1995-10-01..2010-09-30 +set train NSE test NSE +UH 0.7529 0.6771 +DIST 0.7465 0.6744 +LSTM 0.6429 0.5573 +LUMPED 0.1987 0.0838 +HYDRODL 0.4730 0.4361 +UH+DIST 0.7608 0.6867 +UH+LSTM 0.7529 0.6974 +UH+LUMPED 0.5953 0.5186 +UH+HYDRODL 0.6945 0.6514 +UH+DIST+LSTM 0.7695 0.7153 +UH+DIST+LSTM+LUMPED+HYDRODL 0.6892 0.6426 + forward selection adds DIST: training median 0.7608 + forward selection adds LSTM: training median 0.7695 +selected on training years: UH+DIST+LSTM (training summed-Q' median 0.7695) + routed, selected set UH+DIST+LSTM NSE median 0.7391 -> 0.7637 pop +0.0246 [+0.0207,+0.0302] paired +0.0182 up 68.8% down 31.2% + routed, selected set UH+DIST+LSTM KGE median 0.7591 -> 0.7590 pop -0.0001 [-0.0055,+0.0056] paired -0.0049 up 46.2% down 53.8% diff --git a/experiments/levers/timing.py b/experiments/levers/timing.py new file mode 100644 index 00000000..18e30a63 --- /dev/null +++ b/experiments/levers/timing.py @@ -0,0 +1,115 @@ +"""Extra lever: a daily timing correction, fitted on TRAINING years, scored on TEST years. + +p'(t) = (1 - |w|) p(t) + |w| p(t - sign(w)) w > 0 delays (blend with yesterday), w < 0 advances (blend with tomorrow) +Grid w in [-0.9, 0.9] step 0.1. Fitted: one global w (max training median NSE), per-HUC2 w, per-gauge w (gauged ceiling), +an attribute GBM on the per-gauge w (out of fold, 10-fold by gauge), and per-gauge (w, k) jointly with the LS scalar. +Usage: timing.py . Writes timing_s.txt / .json. +""" +import json +import sys + +import numpy as np +import pandas as pd +from sklearn.ensemble import HistGradientBoostingRegressor +from sklearn.model_selection import KFold + +import common as C + +seed, replay = int(sys.argv[1]), sys.argv[2] +src = {42: C.S42, 43: C.S43}[seed] +out, res = [], {} +say = out.append +ids, tt, PT, OT = C.load_preds(src) +rid, tr, PR, OR = C.load_preds(replay) +PR, OR = C.align(rid, PR, ids), C.align(rid, OR, ids) +wy_t, wy_r = C.water_year(tt), C.water_year(tr) +import os +TWY = [int(x) for x in os.environ.get("LEVERS_TRAIN_WY", "1982,1995").split(",")] # smoke-test override only +trn = (wy_r >= TWY[0]) & (wy_r <= TWY[1]) & (tr >= pd.Timestamp("1981-11-01")) +tst = (wy_t >= 1996) & (wy_t <= 2010) +W = np.round(np.arange(-0.9, 0.9001, 0.1), 2) + + +def shift(P, w): + if w == 0: + return P + S = np.full_like(P, np.nan) + if w > 0: + S[:, 1:] = P[:, :-1] + else: + S[:, :-1] = P[:, 1:] + S = np.where(np.isfinite(S), S, P) + return (1 - abs(w)) * P + abs(w) * S + + +def windowed(P, O, mask): + return P[:, mask], O[:, mask] + + +# shift on the full series, then cut the window (so the first test day uses the day before) +NTR = np.stack([C.metrics(*windowed(shift(PR, w), OR, trn)).nse.values for w in W]) # [w, g] +KTR = np.stack([C.ls_scale(*windowed(shift(PR, w), OR, trn)) for w in W]) +TE = {w: shift(PT, w)[:, tst] for w in W} +Ote = OT[:, tst] +base = C.metrics(TE[0.0], Ote) +bn, bk = base.nse.values, base.kge.values +say(f"== seed {seed}: timing blend, fit WY1982-1995, score WY1996-2010; base test median NSE {np.nanmedian(bn):.4f}") + + +def score(wg, name, kg=None): + wg = np.asarray(wg, dtype=float) + P = np.empty_like(Ote) + for w in W: + rows = np.isclose(wg, w) + if rows.any(): + P[rows] = TE[w][rows] + if kg is not None: + P = P * np.where(np.isfinite(kg), kg, 1.0)[:, None] + M = C.metrics(P, Ote) + s, sk = C.summarize(name, M.nse.values, bn), C.summarize(name, M.kge.values, bk, "KGE") + say(C.fmt(s)) + say(C.fmt(sk)) + res[name] = dict(nse=s, kge=sk) + + +med = np.nanmedian(NTR, axis=1) +wg_glob = W[int(np.nanargmax(med))] +say(f" training median NSE by w: " + " ".join(f"{w:+.1f}:{m:.4f}" for w, m in zip(W, med))) +score(np.full(len(ids), wg_glob), f"global w {wg_glob:+.1f}") +G = pd.read_csv(C.HERE / "gauges_table.csv", dtype={"STAID": str, "HUC02": str}).set_index("STAID").reindex(ids) +huc = G.HUC02.values +wh = np.zeros(len(ids)) +for h in sorted(set(huc)): + rows = huc == h + wh[rows] = W[int(np.nanargmax(np.nanmedian(NTR[:, rows], axis=1)))] +score(wh, "per-HUC2 w") +ok = np.isfinite(NTR).all(0) +wg = np.where(ok, W[np.nanargmax(np.where(np.isfinite(NTR), NTR, -9), axis=0)], 0.0) +say(f" per-gauge w: share delay {np.mean(wg > 0):.3f}, advance {np.mean(wg < 0):.3f}, none {np.mean(wg == 0):.3f}; " + f"median |w| {np.median(np.abs(wg)):.2f}") +FEAT = ["up_aridity", "up_meanP", "up_meanslope", "out_log10_uparea", "up_snow_fraction", "up_seasonality_P", + "longest_path_km", "n_reach", "LAT_GAGE", "LNG_GAGE", "up_NDVI", "up_Porosity"] +X = G[FEAT].values.astype(float) +X = np.where(np.isfinite(X), X, np.nanmedian(X, axis=0)) +pred = np.zeros(len(ids)) +for a, b in KFold(10, shuffle=True, random_state=0).split(X): + fa = a[ok[a]] + m = HistGradientBoostingRegressor(max_iter=200, learning_rate=0.05, min_samples_leaf=40, random_state=0) + m.fit(X[fa], wg[fa]) + pred[b] = m.predict(X[b]) +wa = W[np.abs(pred[:, None] - W[None, :]).argmin(1)] +r2 = 1 - np.mean((pred[ok] - wg[ok]) ** 2) / np.var(wg[ok]) +say(f" attribute GBM on per-gauge w, out-of-fold R2 {r2:.3f}") +score(wa, "attribute GBM w (10-fold by gauge)") +score(wg, "per-gauge w fitted on training years (gauged ceiling)") +kj = KTR[np.nanargmax(np.where(np.isfinite(NTR), NTR, -9), axis=0), np.arange(len(ids))] +# joint (w, k) per gauge: choose w maximizing training NSE after its own LS scalar +NTRK = np.stack([C.metrics(*windowed(shift(PR, w) * KTR[i][:, None], OR, trn)).nse.values for i, w in enumerate(W)]) +ij = np.nanargmax(np.where(np.isfinite(NTRK), NTRK, -9), axis=0) +wj = np.where(ok, W[ij], 0.0) +kj = np.where(ok, KTR[ij, np.arange(len(ids))], 1.0) +kj = np.where((kj > 0.2) & (kj < 5), kj, 1.0) +score(wj, "per-gauge (w, k) jointly, training years (gauged ceiling)", kg=kj) +open(C.HERE / f"timing_s{seed}.txt", "w").write("\n".join(out) + "\n") +json.dump(res, open(C.HERE / f"timing_s{seed}.json", "w"), indent=1, default=float) +print("\n".join(out)) diff --git a/experiments/levers/timing_s42.json b/experiments/levers/timing_s42.json new file mode 100644 index 00000000..3dc6a9f3 --- /dev/null +++ b/experiments/levers/timing_s42.json @@ -0,0 +1,142 @@ +{ + "global w -0.0": { + "nse": { + "name": "global w -0.0", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7391119296554918, + "pop_change": 0.0, + "ci_lo": 0.0, + "ci_hi": 0.0, + "paired_median": 0.0, + "share_up": 0.0, + "share_down": 0.0, + "n": 2365 + }, + "kge": { + "name": "global w -0.0", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7590734825696117, + "pop_change": 0.0, + "ci_lo": 0.0, + "ci_hi": 0.0, + "paired_median": 0.0, + "share_up": 0.0, + "share_down": 0.0, + "n": 2365 + } + }, + "per-HUC2 w": { + "nse": { + "name": "per-HUC2 w", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.740019422967113, + "pop_change": 0.0009074933116212103, + "ci_lo": -0.0007615250959527197, + "ci_hi": 0.002890467871171952, + "paired_median": 0.0, + "share_up": 0.2427061310782241, + "share_down": 0.19069767441860466, + "n": 2365 + }, + "kge": { + "name": "per-HUC2 w", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7582569404396404, + "pop_change": -0.0008165421299712872, + "ci_lo": -0.001988570768850112, + "ci_hi": 0.0012265616993939851, + "paired_median": 0.0, + "share_up": 0.17801268498942918, + "share_down": 0.2553911205073996, + "n": 2365 + } + }, + "attribute GBM w (10-fold by gauge)": { + "nse": { + "name": "attribute GBM w (10-fold by gauge)", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7405463326772217, + "pop_change": 0.0014344030217299242, + "ci_lo": -0.0012666330925333064, + "ci_hi": 0.004198774132240409, + "paired_median": 0.0, + "share_up": 0.41226215644820297, + "share_down": 0.3564482029598309, + "n": 2365 + }, + "kge": { + "name": "attribute GBM w (10-fold by gauge)", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7565264269163791, + "pop_change": -0.002547055653232655, + "ci_lo": -0.004829505055142286, + "ci_hi": -0.00013590658280910331, + "paired_median": 0.0, + "share_up": 0.2858350951374207, + "share_down": 0.4828752642706131, + "n": 2365 + } + }, + "per-gauge w fitted on training years (gauged ceiling)": { + "nse": { + "name": "per-gauge w fitted on training years (gauged ceiling)", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7473472895637141, + "pop_change": 0.008235359908222328, + "ci_lo": 0.005612799547868444, + "ci_hi": 0.012579780582337448, + "paired_median": 0.0007577856882642342, + "share_up": 0.5323467230443975, + "share_down": 0.18604651162790697, + "n": 2365 + }, + "kge": { + "name": "per-gauge w fitted on training years (gauged ceiling)", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7617255213857512, + "pop_change": 0.0026520388161395125, + "ci_lo": 0.0004330288759463147, + "ci_hi": 0.005971883093632762, + "paired_median": 0.0, + "share_up": 0.359830866807611, + "share_down": 0.3585623678646934, + "n": 2365 + } + }, + "per-gauge (w, k) jointly, training years (gauged ceiling)": { + "nse": { + "name": "per-gauge (w, k) jointly, training years (gauged ceiling)", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7551224188448988, + "pop_change": 0.016010489189407062, + "ci_lo": 0.01178401288514513, + "ci_hi": 0.020744690693165736, + "paired_median": 0.007550468604872074, + "share_up": 0.654122621564482, + "share_down": 0.34545454545454546, + "n": 2365 + }, + "kge": { + "name": "per-gauge (w, k) jointly, training years (gauged ceiling)", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7714627403570828, + "pop_change": 0.01238925778747113, + "ci_lo": 0.006534517618899266, + "ci_hi": 0.01830082156779231, + "paired_median": 0.002934418237523828, + "share_up": 0.5463002114164904, + "share_down": 0.453276955602537, + "n": 2365 + } + } +} \ No newline at end of file diff --git a/experiments/levers/timing_s42.txt b/experiments/levers/timing_s42.txt new file mode 100644 index 00000000..64f0c0ba --- /dev/null +++ b/experiments/levers/timing_s42.txt @@ -0,0 +1,14 @@ +== seed 42: timing blend, fit WY1982-1995, score WY1996-2010; base test median NSE 0.7391 + training median NSE by w: -0.9:0.6081 -0.8:0.6412 -0.7:0.6722 -0.6:0.7006 -0.5:0.7250 -0.4:0.7426 -0.3:0.7599 -0.2:0.7735 -0.1:0.7819 -0.0:0.7847 +0.1:0.7834 +0.2:0.7771 +0.3:0.7666 +0.4:0.7513 +0.5:0.7318 +0.6:0.7132 +0.7:0.6917 +0.8:0.6647 +0.9:0.6351 + global w -0.0 NSE median 0.7391 -> 0.7391 pop +0.0000 [+0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + global w -0.0 KGE median 0.7591 -> 0.7591 pop +0.0000 [+0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + per-HUC2 w NSE median 0.7391 -> 0.7400 pop +0.0009 [-0.0008,+0.0029] paired +0.0000 up 24.3% down 19.1% + per-HUC2 w KGE median 0.7591 -> 0.7583 pop -0.0008 [-0.0020,+0.0012] paired +0.0000 up 17.8% down 25.5% + per-gauge w: share delay 0.416, advance 0.302, none 0.282; median |w| 0.20 + attribute GBM on per-gauge w, out-of-fold R2 0.088 + attribute GBM w (10-fold by gauge) NSE median 0.7391 -> 0.7405 pop +0.0014 [-0.0013,+0.0042] paired +0.0000 up 41.2% down 35.6% + attribute GBM w (10-fold by gauge) KGE median 0.7591 -> 0.7565 pop -0.0025 [-0.0048,-0.0001] paired +0.0000 up 28.6% down 48.3% + per-gauge w fitted on training years (gauged ceiling) NSE median 0.7391 -> 0.7473 pop +0.0082 [+0.0056,+0.0126] paired +0.0008 up 53.2% down 18.6% + per-gauge w fitted on training years (gauged ceiling) KGE median 0.7591 -> 0.7617 pop +0.0027 [+0.0004,+0.0060] paired +0.0000 up 36.0% down 35.9% + per-gauge (w, k) jointly, training years (gauged ceiling) NSE median 0.7391 -> 0.7551 pop +0.0160 [+0.0118,+0.0207] paired +0.0076 up 65.4% down 34.5% + per-gauge (w, k) jointly, training years (gauged ceiling) KGE median 0.7591 -> 0.7715 pop +0.0124 [+0.0065,+0.0183] paired +0.0029 up 54.6% down 45.3% diff --git a/experiments/levers/timing_s43.json b/experiments/levers/timing_s43.json new file mode 100644 index 00000000..c89651b9 --- /dev/null +++ b/experiments/levers/timing_s43.json @@ -0,0 +1,142 @@ +{ + "global w -0.0": { + "nse": { + "name": "global w -0.0", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7341072383216395, + "pop_change": 0.0, + "ci_lo": 0.0, + "ci_hi": 0.0, + "paired_median": 0.0, + "share_up": 0.0, + "share_down": 0.0, + "n": 2365 + }, + "kge": { + "name": "global w -0.0", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7587949000037071, + "pop_change": 0.0, + "ci_lo": 0.0, + "ci_hi": 0.0, + "paired_median": 0.0, + "share_up": 0.0, + "share_down": 0.0, + "n": 2365 + } + }, + "per-HUC2 w": { + "nse": { + "name": "per-HUC2 w", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7344652468309008, + "pop_change": 0.00035800850926126504, + "ci_lo": -0.0014773992507431833, + "ci_hi": 0.002221985948485989, + "paired_median": 0.0, + "share_up": 0.2714587737843552, + "share_down": 0.20465116279069767, + "n": 2365 + }, + "kge": { + "name": "per-HUC2 w", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.75928044734554, + "pop_change": 0.0004855473418329259, + "ci_lo": -0.0026372740439734948, + "ci_hi": 0.0015549688201454358, + "paired_median": 0.0, + "share_up": 0.1970401691331924, + "share_down": 0.27906976744186046, + "n": 2365 + } + }, + "attribute GBM w (10-fold by gauge)": { + "nse": { + "name": "attribute GBM w (10-fold by gauge)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.736212316148342, + "pop_change": 0.0021050778267025505, + "ci_lo": -0.0003005786982869352, + "ci_hi": 0.00472116625160397, + "paired_median": 0.0, + "share_up": 0.44904862579281185, + "share_down": 0.34291754756871035, + "n": 2365 + }, + "kge": { + "name": "attribute GBM w (10-fold by gauge)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7568116704739036, + "pop_change": -0.001983229529803454, + "ci_lo": -0.006193135385943138, + "ci_hi": -0.0006682929874086442, + "paired_median": 0.0, + "share_up": 0.3019027484143763, + "share_down": 0.4900634249471459, + "n": 2365 + } + }, + "per-gauge w fitted on training years (gauged ceiling)": { + "nse": { + "name": "per-gauge w fitted on training years (gauged ceiling)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7443541689508715, + "pop_change": 0.010246930629231987, + "ci_lo": 0.007543697167891017, + "ci_hi": 0.013915798081839292, + "paired_median": 0.0016134864755924516, + "share_up": 0.5513742071881607, + "share_down": 0.1733615221987315, + "n": 2365 + }, + "kge": { + "name": "per-gauge w fitted on training years (gauged ceiling)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7612799416850426, + "pop_change": 0.0024850416813355647, + "ci_lo": -0.0012796635159703195, + "ci_hi": 0.0044703875378384016, + "paired_median": 0.0, + "share_up": 0.3699788583509514, + "share_down": 0.3547568710359408, + "n": 2365 + } + }, + "per-gauge (w, k) jointly, training years (gauged ceiling)": { + "nse": { + "name": "per-gauge (w, k) jointly, training years (gauged ceiling)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7533583639548792, + "pop_change": 0.01925112563323972, + "ci_lo": 0.01407361120100693, + "ci_hi": 0.023088253272370674, + "paired_median": 0.00872773656100867, + "share_up": 0.6646934460887949, + "share_down": 0.33488372093023255, + "n": 2365 + }, + "kge": { + "name": "per-gauge (w, k) jointly, training years (gauged ceiling)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7696290550038059, + "pop_change": 0.010834155000098766, + "ci_lo": 0.004060471848901908, + "ci_hi": 0.015596024581297304, + "paired_median": 0.0031483261822206066, + "share_up": 0.5479915433403806, + "share_down": 0.4515856236786469, + "n": 2365 + } + } +} \ No newline at end of file diff --git a/experiments/levers/timing_s43.txt b/experiments/levers/timing_s43.txt new file mode 100644 index 00000000..a3f996eb --- /dev/null +++ b/experiments/levers/timing_s43.txt @@ -0,0 +1,14 @@ +== seed 43: timing blend, fit WY1982-1995, score WY1996-2010; base test median NSE 0.7341 + training median NSE by w: -0.9:0.6097 -0.8:0.6412 -0.7:0.6698 -0.6:0.6971 -0.5:0.7212 -0.4:0.7401 -0.3:0.7565 -0.2:0.7690 -0.1:0.7782 -0.0:0.7822 +0.1:0.7794 +0.2:0.7739 +0.3:0.7652 +0.4:0.7490 +0.5:0.7299 +0.6:0.7122 +0.7:0.6920 +0.8:0.6649 +0.9:0.6347 + global w -0.0 NSE median 0.7341 -> 0.7341 pop +0.0000 [+0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + global w -0.0 KGE median 0.7588 -> 0.7588 pop +0.0000 [+0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + per-HUC2 w NSE median 0.7341 -> 0.7345 pop +0.0004 [-0.0015,+0.0022] paired +0.0000 up 27.1% down 20.5% + per-HUC2 w KGE median 0.7588 -> 0.7593 pop +0.0005 [-0.0026,+0.0016] paired +0.0000 up 19.7% down 27.9% + per-gauge w: share delay 0.430, advance 0.295, none 0.275; median |w| 0.20 + attribute GBM on per-gauge w, out-of-fold R2 0.100 + attribute GBM w (10-fold by gauge) NSE median 0.7341 -> 0.7362 pop +0.0021 [-0.0003,+0.0047] paired +0.0000 up 44.9% down 34.3% + attribute GBM w (10-fold by gauge) KGE median 0.7588 -> 0.7568 pop -0.0020 [-0.0062,-0.0007] paired +0.0000 up 30.2% down 49.0% + per-gauge w fitted on training years (gauged ceiling) NSE median 0.7341 -> 0.7444 pop +0.0102 [+0.0075,+0.0139] paired +0.0016 up 55.1% down 17.3% + per-gauge w fitted on training years (gauged ceiling) KGE median 0.7588 -> 0.7613 pop +0.0025 [-0.0013,+0.0045] paired +0.0000 up 37.0% down 35.5% + per-gauge (w, k) jointly, training years (gauged ceiling) NSE median 0.7341 -> 0.7534 pop +0.0193 [+0.0141,+0.0231] paired +0.0087 up 66.5% down 33.5% + per-gauge (w, k) jointly, training years (gauged ceiling) KGE median 0.7588 -> 0.7696 pop +0.0108 [+0.0041,+0.0156] paired +0.0031 up 54.8% down 45.2% diff --git a/experiments/levers/variance.json b/experiments/levers/variance.json new file mode 100644 index 00000000..d14e1fc4 --- /dev/null +++ b/experiments/levers/variance.json @@ -0,0 +1,499 @@ +{ + "curve::seed42 n0+gamma (07-29-47Z)": { + "slope_41_50": 0.0019376757972964335, + "se_41_50": 0.0035227447721296033, + "level_41_50": 0.298029509936575, + "slope_31_40": 0.0015954530360689392, + "se_31_40": 0.002904694124928693, + "level_31_40": 0.2999491746723045, + "slope_11_20": 0.0036263633160356222, + "level_11_20": 0.3038274141649049, + "early": 0.2840816472727272 + }, + "curve::seed43 n0+gamma (10-31-30Z)": { + "slope_41_50": -0.09349678672816965, + "se_41_50": 0.10607047063174017, + "level_41_50": 0.5955850430021141, + "slope_31_40": -0.003730727095906203, + "se_31_40": 0.003325425668802571, + "level_31_40": 0.3085044424947146, + "slope_11_20": -0.005674167769876349, + "level_11_20": 0.2917270130232558, + "early": 0.284635084820296 + }, + "curve::gamma=0, n+p+q (09-12T03-53-34Z)": { + "slope_41_50": 0.0017313024383368597, + "se_41_50": 0.0035051601354462226, + "level_41_50": 0.291348844820296, + "slope_31_40": 0.0021167575603818355, + "se_31_40": 0.003078660286965507, + "level_31_40": 0.29450638186046507, + "slope_11_20": 0.003499348450253057, + "level_11_20": 0.29776414693446085, + "early": 0.278639595179704 + }, + "curve::n only (09-12T23-39-06Z)": { + "slope_41_50": 0.0021292760561214714, + "se_41_50": 0.0035273548164941626, + "level_41_50": 0.2963910100211416, + "slope_31_40": 0.001963158721250565, + "se_31_40": 0.003047821188179843, + "level_31_40": 0.29863320871035937, + "slope_11_20": 0.0037565177269524, + "level_11_20": 0.30081751424947145, + "early": 0.2847412736575053 + }, + "single::2026-09-10T21-21-48Z-conus-train-and-test": { + "nse": { + "name": "single 2026-09-10T21-21-48Z", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7375664913457005, + "pop_change": -0.0015454383097921465, + "ci_lo": -0.0041667299254533495, + "ci_hi": 0.0011625039684621473, + "paired_median": 0.00011006279858083445, + "share_up": 0.5192389006342495, + "share_down": 0.4807610993657505, + "n": 2365 + }, + "kge": { + "name": "single 2026-09-10T21-21-48Z", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7599557564229085, + "pop_change": 0.0008822738532973107, + "ci_lo": -0.0016511579768828638, + "ci_hi": 0.0031674720856946694, + "paired_median": -2.1365467106404523e-05, + "share_up": 0.4896405919661734, + "share_down": 0.5103594080338266, + "n": 2365 + } + }, + "single::2026-09-11T06-38-49Z-conus-train-and-test": { + "nse": { + "name": "single 2026-09-11T06-38-49Z", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7383377610782021, + "pop_change": -0.0007741685772905127, + "ci_lo": -0.0032934337210765554, + "ci_hi": 0.0030028245035391, + "paired_median": -0.00011595810749798208, + "share_up": 0.479492600422833, + "share_down": 0.5205073995771671, + "n": 2365 + }, + "kge": { + "name": "single 2026-09-11T06-38-49Z", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7609741382182067, + "pop_change": 0.0019006556485955395, + "ci_lo": -0.0009470993813171846, + "ci_hi": 0.004516687109743622, + "paired_median": -0.00017470942432595837, + "share_up": 0.45961945031712476, + "share_down": 0.5403805496828753, + "n": 2365 + } + }, + "single::2026-09-12T03-53-34Z-train-and-test": { + "nse": { + "name": "single 2026-09-12T03-53-34Z", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7452775323246579, + "pop_change": 0.0061656026691652155, + "ci_lo": 0.0034729839482855627, + "ci_hi": 0.010258671927280591, + "paired_median": 0.0006394888049867653, + "share_up": 0.572938689217759, + "share_down": 0.427061310782241, + "n": 2365 + }, + "kge": { + "name": "single 2026-09-12T03-53-34Z", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7622927025403281, + "pop_change": 0.0032192199707169467, + "ci_lo": 0.0009819143247148387, + "ci_hi": 0.005742849807732431, + "paired_median": 0.00018853237879346008, + "share_up": 0.5479915433403806, + "share_down": 0.4520084566596195, + "n": 2365 + } + }, + "single::2026-09-12T06-06-19Z-train-and-test": { + "nse": { + "name": "single 2026-09-12T06-06-19Z", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7363939901920158, + "pop_change": -0.0027179394634768705, + "ci_lo": -0.005101468367716353, + "ci_hi": 0.0003894130882615802, + "paired_median": -0.00012154456356006094, + "share_up": 0.46680761099365753, + "share_down": 0.5331923890063425, + "n": 2365 + }, + "kge": { + "name": "single 2026-09-12T06-06-19Z", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7585801708160331, + "pop_change": -0.0004933117535780296, + "ci_lo": -0.0035807426912285167, + "ci_hi": 0.0016921988694449386, + "paired_median": -0.0002943131078132488, + "share_up": 0.42029598308668076, + "share_down": 0.5797040169133192, + "n": 2365 + } + }, + "single::2026-09-12T16-30-14Z-train-and-test": { + "nse": { + "name": "single 2026-09-12T16-30-14Z", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7416052231768633, + "pop_change": 0.002493293521370621, + "ci_lo": 0.00021431456602042656, + "ci_hi": 0.006342933326679666, + "paired_median": 0.00019043243982730385, + "share_up": 0.5285412262156448, + "share_down": 0.4714587737843552, + "n": 2365 + }, + "kge": { + 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+ "single::2026-09-12T23-39-06Z-train-and-test": { + "nse": { + "name": "single 2026-09-12T23-39-06Z", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7409165798527144, + "pop_change": 0.0018046501972217577, + "ci_lo": -0.000511146318035649, + "ci_hi": 0.004854496037369202, + "paired_median": 0.0003894465421476756, + "share_up": 0.5797040169133192, + "share_down": 0.42029598308668076, + "n": 2365 + }, + "kge": { + "name": "single 2026-09-12T23-39-06Z", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7612137576132139, + "pop_change": 0.002140275043602702, + "ci_lo": 4.8202229744506755e-05, + "ci_hi": 0.004259286869361145, + "paired_median": 0.00017030410624985226, + "share_up": 0.5716701902748414, + "share_down": 0.42832980972515855, + "n": 2365 + } + }, + "single::2026-09-27T07-29-47Z-train-and-test": { + "nse": { + "name": "single 2026-09-27T07-29-47Z", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7391119296554927, + "pop_change": 0.0, + "ci_lo": 0.0, + "ci_hi": 0.0, + "paired_median": 0.0, + "share_up": 0.0, + "share_down": 0.0, + "n": 2365 + }, + "kge": { + "name": "single 2026-09-27T07-29-47Z", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7590734825696112, + "pop_change": 0.0, + "ci_lo": 0.0, + "ci_hi": 0.0, + "paired_median": 0.0, + "share_up": 0.0, + "share_down": 0.0, + "n": 2365 + } + }, + "single::2026-09-27T10-31-30Z-train-and-test": { + "nse": { + "name": "single 2026-09-27T10-31-30Z", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7341072383216387, + "pop_change": -0.00500469133385395, + "ci_lo": -0.007360166959033523, + "ci_hi": -0.0018396745761168347, + "paired_median": -0.000681366729885502, + "share_up": 0.40211416490486257, + "share_down": 0.5978858350951374, + "n": 2365 + }, + "kge": { + "name": "single 2026-09-27T10-31-30Z", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7587949000037093, + "pop_change": -0.0002785825659018526, + "ci_lo": -0.0018132733472217555, + "ci_hi": 0.0030024733985197782, + "paired_median": -0.00020257340941687296, + "share_up": 0.44608879492600423, + "share_down": 0.5539112050739958, + "n": 2365 + } + }, + "single::2026-09-13T17-22-30Z-train-and-test": { + "nse": { + "name": "single 2026-09-13T17-22-30Z", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7300111306185426, + "pop_change": -0.009100799036950047, + "ci_lo": -0.01384916620166525, + "ci_hi": -0.003943726690148064, + "paired_median": -0.006150323259829449, + "share_up": 0.4147991543340381, + "share_down": 0.585200845665962, + "n": 2365 + }, + "kge": { + "name": "single 2026-09-13T17-22-30Z", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7557419137014604, + "pop_change": -0.0033315688681507805, + "ci_lo": -0.009067987922541534, + "ci_hi": 0.0018132946381890177, + "paired_median": -0.006691914049072323, + "share_up": 0.44059196617336155, + "share_down": 0.5594080338266385, + "n": 2365 + } + }, + "ens::two seeds of the working recipe (s42+s43)": { + "nse": { + "name": "ens two seeds of the working recipe (s42+s43)", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7376058960995027, + "pop_change": -0.001506033555989994, + "ci_lo": -0.003572716251610264, + "ci_hi": 0.0008647845539724608, + "paired_median": -9.925938179478067e-05, + "share_up": 0.4642706131078224, + "share_down": 0.5357293868921776, + "n": 2365 + }, + "kge": { + "name": "ens two seeds of the working recipe (s42+s43)", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7606690561521232, + "pop_change": 0.0015955735825120554, + "ci_lo": -0.000748016187778322, + "ci_hi": 0.003802247057779582, + "paired_median": -5.560366785317683e-05, + "share_up": 0.4638477801268499, + "share_down": 0.5361522198731501, + "n": 2365 + }, + "mean_member_median": 0.7366095839885657 + }, + "ens::working recipe on two Q' stores (UH + dhbv2-dist aorc2f)": { + "nse": { + "name": "ens working recipe on two Q' stores (UH + dhbv2-dist aorc2f)", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7430471587437439, + "pop_change": 0.00393522908825128, + "ci_lo": 0.00083110232832041, + "ci_hi": 0.008588719307215463, + "paired_median": 0.0044046947778852985, + "share_up": 0.6101479915433404, + "share_down": 0.3898520084566596, + "n": 2365 + }, + "kge": { + "name": "ens working recipe on two Q' stores (UH + dhbv2-dist aorc2f)", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7630903451465978, + "pop_change": 0.004016862576986635, + "ci_lo": -9.599108678119415e-05, + "ci_hi": 0.007886252556088236, + "paired_median": 0.0009479092846090831, + "share_up": 0.5179704016913319, + "share_down": 0.4820295983086681, + "n": 2365 + }, + "mean_member_median": 0.7345615301370176 + }, + "ens::two seeds + dhbv2-dist aorc2f (3 members)": { + "nse": { + "name": "ens two seeds + dhbv2-dist aorc2f (3 members)", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7437692153264461, + "pop_change": 0.0046572856709534705, + "ci_lo": 0.0014857638171178965, + "ci_hi": 0.008795198790785344, + "paired_median": 0.00475174738987727, + "share_up": 0.6583509513742072, + "share_down": 0.3416490486257928, + "n": 2365 + }, + "kge": { + "name": "ens two seeds + dhbv2-dist aorc2f (3 members)", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7645603542222856, + "pop_change": 0.005486871652674474, + "ci_lo": 0.0018645126528541388, + "ci_hi": 0.009171190769358545, + "paired_median": 0.001488680373193163, + "share_up": 0.5331923890063425, + "share_down": 0.46680761099365753, + "n": 2365 + }, + "mean_member_median": 0.7344100995318913 + }, + "ens::all 10 UH-store 50-epoch nse-batch variants": { + "nse": { + "name": "ens all 10 UH-store 50-epoch nse-batch variants", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.7435801927692454, + "pop_change": 0.004468263113752702, + "ci_lo": 0.0024745196542328763, + "ci_hi": 0.008039275874642742, + "paired_median": 0.0006753556901422098, + "share_up": 0.6219873150105708, + "share_down": 0.3780126849894292, + "n": 2365 + }, + "kge": { + "name": "ens all 10 UH-store 50-epoch nse-batch variants", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7616376534839847, + "pop_change": 0.0025641709143735536, + "ci_lo": 0.0006064353425655478, + "ci_hi": 0.005175964622906326, + "paired_median": 8.456225028430353e-06, + "share_up": 0.5040169133192389, + "share_down": 0.4959830866807611, + "n": 2365 + }, + "mean_member_median": 0.740036293543319 + }, + "ens::UH variants without the three constant-gamma arms (7)": { + "nse": { + "name": "ens UH variants without the three constant-gamma arms (7)", + "metric": "NSE", + "median_base": 0.7391119296554927, + "median_new": 0.743108427346583, + "pop_change": 0.003996497691090339, + "ci_lo": 0.0019099855309784002, + "ci_hi": 0.007352177854365097, + "paired_median": 0.0007180464942115883, + "share_up": 0.6363636363636364, + "share_down": 0.36363636363636365, + "n": 2365 + }, + "kge": { + "name": "ens UH variants without the three constant-gamma arms (7)", + "metric": "KGE", + "median_base": 0.7590734825696112, + "median_new": 0.7617064456137375, + "pop_change": 0.002632963044126302, + "ci_lo": 0.0007250985485406897, + "ci_hi": 0.005145427827414703, + "paired_median": 5.3097991167150305e-05, + "share_up": 0.5260042283298098, + "share_down": 0.4739957716701903, + "n": 2365 + }, + "mean_member_median": 0.7395603936793241 + } +} \ No newline at end of file diff --git a/experiments/levers/variance.py b/experiments/levers/variance.py new file mode 100644 index 00000000..f6336c1b --- /dev/null +++ b/experiments/levers/variance.py @@ -0,0 +1,242 @@ +"""Lever 2: routing-head convergence and variance, from existing runs only (test years WY1996-2010, 2,365 gauges). + +A. Loss curves from run.log: per-epoch gauge-weighted mean micro-batch loss (nse-batch), OLS slope over epochs 41-50 + (and 31-40) with its standard error; median n per epoch. +B. Parameter drift from plot/kan_parameters_epoch{25,50}.nc (reach level) against the seed-to-seed difference. +C. Ensembles (equal-weight mean of routed predictions, no fitted weights): two seeds of the working recipe; the working + recipe on two Q' stores; every UH-store 50-epoch nse-batch variant. Scored on test years, paired against seed 42. +D. The gauges just below the population median (within 0.03): what the seed change does there and who they are. +Writes variance.txt and variance.json, per-gauge table variance_by_gauge.csv. +""" +import json +import re + +import numpy as np +import pandas as pd +import xarray as xr +from scipy import stats + +import common as C + +out, res = [], {} +say = out.append + +# ---------------- A. loss curves ---------------- +RX_EP = re.compile(r"\] epoch (\d+) lr=([0-9.e-]+)") +RX_MI = re.compile(r"micro \d+/\d+ gauges=(\d+) loss=([0-9.eE+-]+) n=\d+ median_n=([0-9.eE+-]+)") + + +def curve(run): + ep, rows = 0, [] + for line in open(C.RUNS / run / "run.log"): + m = RX_EP.search(line) + if m: + ep = int(m.group(1)) + continue + m = RX_MI.search(line) + if m and ep: + rows.append((ep, int(m.group(1)), float(m.group(2)), float(m.group(3)))) + d = pd.DataFrame(rows, columns=["epoch", "g", "loss", "median_n"]) + e = d.groupby("epoch").apply(lambda x: pd.Series(dict(loss=np.average(x.loss, weights=x.g), n_micro=len(x), + median_n=x.median_n.iloc[-1], + loss_med=x.loss.median()))) + return e + + +def slope(e, lo, hi, col="loss"): + x = e.loc[lo:hi] + r = stats.linregress(x.index.values.astype(float), x[col].values) + return r.slope, r.stderr, x[col].mean() + + +say("== A. training loss (nse-batch, gauge-weighted mean over each epoch's micro-batches; random 90-day windows)") +CURVES = {"seed42 n0+gamma (07-29-47Z)": C.S42, "seed43 n0+gamma (10-31-30Z)": C.S43, + "gamma=0, n+p+q (09-12T03-53-34Z)": "2026-09-12T03-53-34Z-train-and-test", + "n only (09-12T23-39-06Z)": "2026-09-12T23-39-06Z-train-and-test"} +for name, run in CURVES.items(): + e = curve(run) + s1, se1, m1 = slope(e, 41, 50) + s2, se2, m2 = slope(e, 31, 40) + s3, se3, m3 = slope(e, 11, 20) + early = e.loss.loc[1:5].mean() + say(f" {name:36s} epochs {int(e.index.max())}: mean loss ep1-5 {early:.4f}, ep11-20 {m3:.4f}, ep31-40 {m2:.4f}, " + f"ep41-50 {m1:.4f}") + say(f" {'':36s} slope/epoch ep11-20 {s3:+.5f}+-{se3:.5f}; ep31-40 {s2:+.5f}+-{se2:.5f}; ep41-50 {s1:+.5f}+-{se1:.5f} " + f"(= {100 * s1 * 10 / m1:+.2f}% of the level over 10 epochs); epoch-to-epoch sd ep41-50 {e.loss.loc[41:50].std():.4f}") + say(f" {'':36s} median n by epoch: " + " ".join(f"e{k}:{e.median_n.get(k, np.nan):.4f}" for k in [1, 5, 10, 20, 30, 40, 45, 50])) + res[f"curve::{name}"] = dict(slope_41_50=s1, se_41_50=se1, level_41_50=m1, slope_31_40=s2, se_31_40=se2, + level_31_40=m2, slope_11_20=s3, level_11_20=m3, early=early) + e.to_csv(C.HERE / f"loss_curve_{run[:20]}.csv") + +# ---------------- A2. paired training loss on identical batches (seed-42 runs share gauge batches and windows) ---- +def micro(run): + ep, rows = 0, [] + for line in open(C.RUNS / run / "run.log"): + m = RX_EP.search(line) + if m: + ep = int(m.group(1)) + continue + m = RX_MI.search(line) + if m and ep: + nn = re.search(r" n=(\d+) ", line).group(1) + rows.append((ep, int(m.group(1)), int(nn), float(m.group(2)))) + return pd.DataFrame(rows, columns=["epoch", "g", "nobs", "loss"]) + + +say("\n== A2. paired training loss on identical micro-batches (seed-42 runs draw the same gauges and windows)") +ref = micro(C.S42) +for name, run in [("gamma=0, n+p+q (09-12T03-53-34Z)", "2026-09-12T03-53-34Z-train-and-test"), + ("gamma=0.1, n+p+q (09-12T20-38-08Z)", "2026-09-12T20-38-08Z-train-and-test"), + ("learned gamma, n+p+q (09-12T16-30-14Z)", "2026-09-12T16-30-14Z-train-and-test"), + ("n only (09-12T23-39-06Z)", "2026-09-12T23-39-06Z-train-and-test")]: + o = micro(run) + same = len(o) == len(ref) and (o.nobs.values == ref.nobs.values).all() and (o.g.values == ref.g.values).all() + if not same: + say(f" {name}: batches differ, skipped") + continue + d = o.loss.values - ref.loss.values + rel = d / ref.loss.values + for lo, hi in [(1, 10), (11, 20), (21, 40), (41, 50)]: + b = (ref.epoch >= lo) & (ref.epoch <= hi) + say(f" {name:40s} ep{lo:>2}-{hi:<2} mean(loss - s42) {d[b].mean():+.4f} +- {d[b].std() / np.sqrt(b.sum()):.4f}, " + f"median rel {np.median(rel[b]):+.3f}, share lower {np.mean(d[b] < 0):.3f} (n={int(b.sum())})") + +# ---------------- B. parameter drift ---------------- +say("\n== B. reach-level parameter drift (KAN forward over all 346,321 CONUS reaches)") + + +def kp(run, ep=None): + f = f"kan_parameters_epoch{ep}.nc" if ep else "kan_parameters.nc" + ds = xr.open_dataset(C.RUNS / run / "plot" / f) + return ds.n.values.astype(float), ds.gamma.values.astype(float), ds.COMID.values + + +n42_10, g42_10, c42 = kp(C.S42, 10) +n42_25, g42_25, _ = kp(C.S42, 25) +n42_50, g42_50, _ = kp(C.S42, 50) +n43_50, g43_50, c43 = kp(C.S43) # seed 43 kept only its final field +assert (c42 == c43).all() + + +def mad_log(a, b): + return float(np.median(np.abs(np.log(a / b)))) + + +say(f" seed42 median n e10 {np.median(n42_10):.4f} e25 {np.median(n42_25):.4f} e50 {np.median(n42_50):.4f}; " + f"gamma e10 {np.median(g42_10):.4f} e25 {np.median(g42_25):.4f} e50 {np.median(g42_50):.4f}") +say(f" seed43 median n e50 {np.median(n43_50):.4f}; gamma e50 {np.median(g43_50):.4f} (only the final field was kept)") +say(f" median |ln n ratio|: seed42 e10->e25 {mad_log(n42_25, n42_10):.3f}, e25->e50 {mad_log(n42_50, n42_25):.3f}; " + f"seed42 vs seed43 at e50 {mad_log(n42_50, n43_50):.3f}") +say(f" median |gamma change|: seed42 e10->e25 {np.median(np.abs(g42_25 - g42_10)):.4f}, e25->e50 " + f"{np.median(np.abs(g42_50 - g42_25)):.4f}; seed42 vs seed43 at e50 {np.median(np.abs(g42_50 - g43_50)):.4f}") +say(f" Spearman(n) seed42 vs seed43 at e50 {stats.spearmanr(n42_50, n43_50).correlation:.3f}; " + f"Spearman(gamma) {stats.spearmanr(g42_50, g43_50).correlation:.3f}") + +# ---------------- C. ensembles ---------------- +say("\n== C. ensembles on the test years (equal-weight mean of routed predictions), paired against seed 42 (0.7391)") +ids, t, P42, O = C.load_preds(C.S42) +M42 = C.metrics(P42, O) +base_n, base_k = M42.nse.values, M42.kge.values +BASE = float(np.nanmedian(base_n)) +preds = {"s42": P42} + + +def get(run): + if run not in preds: + i, tt, P, _ = C.load_preds(run) + assert len(tt) == len(t) and (tt == t).all() + preds[run] = C.align(i, P, ids) + return preds[run] + + +UH = ["2026-09-10T21-21-48Z-conus-train-and-test", "2026-09-11T06-38-49Z-conus-train-and-test", + "2026-09-12T03-53-34Z-train-and-test", "2026-09-12T06-06-19Z-train-and-test", "2026-09-12T16-30-14Z-train-and-test", + "2026-09-12T20-36-00Z-train-and-test", "2026-09-12T20-38-08Z-train-and-test", "2026-09-12T23-39-06Z-train-and-test", + C.S42, C.S43] +singles = {} +for r in UH + ["2026-09-13T17-22-30Z-train-and-test"]: + M = C.metrics(get(r) if r != C.S42 else P42, O) + singles[r] = M + s = C.summarize(f"single {r[:20]}", M.nse, base_n) + sk = C.summarize(f"single {r[:20]}", M.kge, base_k, "KGE") + say(C.fmt(s)) + say(C.fmt(sk)) + res[f"single::{r}"] = dict(nse=s, kge=sk) + +ENS = { + "two seeds of the working recipe (s42+s43)": [C.S42, C.S43], + "working recipe on two Q' stores (UH + dhbv2-dist aorc2f)": [C.S42, "2026-09-13T17-22-30Z-train-and-test"], + "two seeds + dhbv2-dist aorc2f (3 members)": [C.S42, C.S43, "2026-09-13T17-22-30Z-train-and-test"], + "all 10 UH-store 50-epoch nse-batch variants": UH, + "UH variants without the three constant-gamma arms (7)": [r for r in UH if r not in ( + "2026-09-12T06-06-19Z-train-and-test", "2026-09-12T20-36-00Z-train-and-test", "2026-09-12T20-38-08Z-train-and-test")], +} +say("") +for name, members in ENS.items(): + Pm = np.mean([get(r) if r != C.S42 else P42 for r in members], axis=0) + M = C.metrics(Pm, O) + s = C.summarize(f"ens {name}", M.nse, base_n) + sk = C.summarize(f"ens {name}", M.kge, base_k, "KGE") + say(C.fmt(s)) + say(C.fmt(sk)) + mean_single = np.mean([np.nanmedian(singles[r].nse) for r in members]) + say(f" mean of the members' own medians {mean_single:.4f}; ensemble minus that {s['median_new'] - mean_single:+.4f}") + res[f"ens::{name}"] = dict(nse=s, kge=sk, mean_member_median=mean_single) + +# recipe effect vs seed noise, paired per gauge +d_seed = singles[C.S43].nse.values - base_n +d_rec = singles["2026-09-12T03-53-34Z-train-and-test"].nse.values - base_n +say("\n per-gauge |dNSE| distribution: seed43-seed42 median |d| " + f"{np.nanmedian(np.abs(d_seed)):.4f}, p90 {np.nanpercentile(np.abs(d_seed), 90):.4f}; " + f"gamma0/n+p+q minus s42 median |d| {np.nanmedian(np.abs(d_rec)):.4f}, p90 {np.nanpercentile(np.abs(d_rec), 90):.4f}") +say(f" sign test gamma0/n+p+q vs s42: up {np.mean(d_rec > 0):.3f}; seed43 vs s42: up {np.mean(d_seed > 0):.3f}") + +# ---------------- D. near-median gauges ---------------- +say("\n== D. gauges within 0.03 below the seed-42 population median") +G = pd.read_csv(C.HERE / "gauges_table.csv", dtype={"STAID": str, "HUC02": str}).set_index("STAID").reindex(ids) +near = (base_n < BASE) & (base_n >= BASE - 0.03) +M43 = singles[C.S43] +dn = M43.nse.values - base_n +ens2 = C.metrics(np.mean([P42, get(C.S43)], axis=0), O).nse.values +T = pd.DataFrame(dict(nse42=base_n, nse43=M43.nse.values, d43=dn, nse_ens2=ens2, kge42=base_k, + kge43=M43.kge.values, beta42=M42.beta.values, alpha42=M42.alpha.values, r42=M42.r.values), + index=ids).join(G) +T["near"] = near +T.to_csv(C.HERE / "variance_by_gauge.csv") +say(f" n near = {int(near.sum())}; in them seed43-seed42 median {np.nanmedian(dn[near]):+.4f}, share down " + f"{np.mean(dn[near] < 0):.3f}; |d| median {np.nanmedian(np.abs(dn[near])):.4f} (population |d| {np.nanmedian(np.abs(dn)):.4f})") +say(f" crossings at the median: s42-below-and-near gauges that seed43 lifts above the s42 median: " + f"{int((near & (M43.nse.values >= BASE)).sum())}; s42-above gauges seed43 drops below: " + f"{int(((base_n >= BASE) & (M43.nse.values < BASE)).sum())}; s42-below that s43 lifts above: " + f"{int(((base_n < BASE) & (M43.nse.values >= BASE)).sum())}") +cols = ["DRAIN_SQKM", "up_aridity", "up_snow_fraction", "tt_days_1ms", "longest_path_km", "n_reach", "nid_dor", + "beta42", "alpha42", "r42"] +lose = near & (dn < np.nanpercentile(dn[near], 25)) +say(" medians: population | near-median | near-median losing most (bottom quartile of seed43-seed42 there, n=" + f"{int(lose.sum())})") +for c in cols: + say(f" {c:18s} {T[c].median():10.3f} | {T.loc[near, c].median():10.3f} | {T.loc[lose, c].median():10.3f}") +say(f" near-median share Ref class {np.mean(T.loc[near, 'CLASS'] == 'Ref'):.3f} vs population " + f"{np.mean(T.CLASS == 'Ref'):.3f}; share with a NID dam >= 10 MCM {np.mean(T.loc[near, 'n_nid_ge10mcm'] > 0):.3f} " + f"vs {np.mean(T.n_nid_ge10mcm > 0):.3f}") +say(" near-median HUC02 counts (top 6): " + ", ".join(f"{k}:{v}" for k, v in T.loc[near, 'HUC02'].value_counts().head(6).items())) +say(" near-median losing-most HUC02 counts: " + ", ".join(f"{k}:{v}" for k, v in T.loc[lose, 'HUC02'].value_counts().head(6).items())) +# what drives seed sensitivity, population-wide +say(" Spearman of |seed43-seed42 NSE| with attributes (population):") +for c in ["DRAIN_SQKM", "up_aridity", "up_snow_fraction", "tt_days_1ms", "n_reach", "nid_dor", "nse42"]: + ok = np.isfinite(T[c]) & np.isfinite(dn) + rho = stats.spearmanr(np.abs(dn[ok]), T[c][ok]).correlation + say(f" {c:18s} {rho:+.3f}") +say(" Spearman of signed (seed43-seed42) NSE with attributes (population):") +for c in ["DRAIN_SQKM", "up_aridity", "up_snow_fraction", "tt_days_1ms"]: + ok = np.isfinite(T[c]) & np.isfinite(dn) + say(f" {c:18s} {stats.spearmanr(dn[ok], T[c][ok]).correlation:+.3f}") +# where the median-seed difference comes from: by area bin +say(" seed43-seed42 by drainage-area bin (median d, share down, n):") +for lo, hi in [(0, 300), (300, 1000), (1000, 3000), (3000, 10000), (10000, 1e7)]: + b = (T.DRAIN_SQKM >= lo) & (T.DRAIN_SQKM < hi) + say(f" {lo:>6}-{hi:<8} {np.nanmedian(dn[b]):+.4f} {np.mean(dn[b] < 0):.3f} {int(b.sum())}") + +open(C.HERE / "variance.txt", "w").write("\n".join(out) + "\n") +json.dump(res, open(C.HERE / "variance.json", "w"), indent=1, default=float) +print("\n".join(out)) diff --git a/experiments/levers/variance.txt b/experiments/levers/variance.txt new file mode 100644 index 00000000..278d64a2 --- /dev/null +++ b/experiments/levers/variance.txt @@ -0,0 +1,118 @@ +== A. training loss (nse-batch, gauge-weighted mean over each epoch's micro-batches; random 90-day windows) + seed42 n0+gamma (07-29-47Z) epochs 50: mean loss ep1-5 0.2841, ep11-20 0.3038, ep31-40 0.2999, ep41-50 0.2980 + slope/epoch ep11-20 +0.00363+-0.00323; ep31-40 +0.00160+-0.00290; ep41-50 +0.00194+-0.00352 (= +6.50% of the level over 10 epochs); epoch-to-epoch sd ep41-50 0.0307 + median n by epoch: e1:0.1171 e5:0.0471 e10:0.0964 e20:0.0759 e30:0.0765 e40:0.0607 e45:0.0594 e50:0.0588 + seed43 n0+gamma (10-31-30Z) epochs 50: mean loss ep1-5 0.2846, ep11-20 0.2917, ep31-40 0.3085, ep41-50 0.5956 + slope/epoch ep11-20 -0.00567+-0.00262; ep31-40 -0.00373+-0.00333; ep41-50 -0.09350+-0.10607 (= -156.98% of the level over 10 epochs); epoch-to-epoch sd ep41-50 0.9514 + median n by epoch: e1:0.1109 e5:0.0607 e10:0.0467 e20:0.0656 e30:0.0714 e40:0.0572 e45:0.0632 e50:0.0629 + gamma=0, n+p+q (09-12T03-53-34Z) epochs 50: mean loss ep1-5 0.2786, ep11-20 0.2978, ep31-40 0.2945, ep41-50 0.2913 + slope/epoch ep11-20 +0.00350+-0.00312; ep31-40 +0.00212+-0.00308; ep41-50 +0.00173+-0.00351 (= +5.94% of the level over 10 epochs); epoch-to-epoch sd ep41-50 0.0305 + median n by epoch: e1:0.1178 e5:0.0525 e10:0.0726 e20:0.0829 e30:0.0845 e40:0.0727 e45:0.0665 e50:0.0672 + n only (09-12T23-39-06Z) epochs 50: mean loss ep1-5 0.2847, ep11-20 0.3008, ep31-40 0.2986, ep41-50 0.2964 + slope/epoch ep11-20 +0.00376+-0.00322; ep31-40 +0.00196+-0.00305; ep41-50 +0.00213+-0.00353 (= +7.18% of the level over 10 epochs); epoch-to-epoch sd ep41-50 0.0309 + median n by epoch: e1:0.1030 e5:0.0539 e10:0.0559 e20:0.0741 e30:0.0675 e40:0.0705 e45:0.0620 e50:0.0643 + +== A2. paired training loss on identical micro-batches (seed-42 runs draw the same gauges and windows) + gamma=0, n+p+q (09-12T03-53-34Z) ep 1-10 mean(loss - s42) -6470.2677 +- 6461.5155, median rel -0.012, share lower 0.692 (n=370) + gamma=0, n+p+q (09-12T03-53-34Z) ep11-20 mean(loss - s42) -0.0060 +- 0.0009, median rel -0.014, share lower 0.751 (n=370) + gamma=0, n+p+q (09-12T03-53-34Z) ep21-40 mean(loss - s42) -0.0054 +- 0.0006, median rel -0.015, share lower 0.720 (n=740) + gamma=0, n+p+q (09-12T03-53-34Z) ep41-50 mean(loss - s42) -0.0067 +- 0.0008, median rel -0.012, share lower 0.738 (n=370) + gamma=0.1, n+p+q (09-12T20-38-08Z) ep 1-10 mean(loss - s42) -6470.2701 +- 6461.5156, median rel -0.015, share lower 0.741 (n=370) + gamma=0.1, n+p+q (09-12T20-38-08Z) ep11-20 mean(loss - s42) -0.0034 +- 0.0013, median rel -0.010, share lower 0.665 (n=370) + gamma=0.1, n+p+q (09-12T20-38-08Z) ep21-40 mean(loss - s42) -0.0047 +- 0.0006, median rel -0.011, share lower 0.723 (n=740) + gamma=0.1, n+p+q (09-12T20-38-08Z) ep41-50 mean(loss - s42) -0.0060 +- 0.0007, median rel -0.011, share lower 0.730 (n=370) + learned gamma, n+p+q (09-12T16-30-14Z) ep 1-10 mean(loss - s42) -6470.2672 +- 6461.5156, median rel -0.007, share lower 0.635 (n=370) + learned gamma, n+p+q (09-12T16-30-14Z) ep11-20 mean(loss - s42) -0.0020 +- 0.0008, median rel -0.005, share lower 0.641 (n=370) + learned gamma, n+p+q (09-12T16-30-14Z) ep21-40 mean(loss - s42) -0.0022 +- 0.0006, median rel -0.006, share lower 0.645 (n=740) + learned gamma, n+p+q (09-12T16-30-14Z) ep41-50 mean(loss - s42) -0.0032 +- 0.0007, median rel -0.007, share lower 0.662 (n=370) + n only (09-12T23-39-06Z) ep 1-10 mean(loss - s42) -6470.2652 +- 6461.5156, median rel -0.007, share lower 0.643 (n=370) + n only (09-12T23-39-06Z) ep11-20 mean(loss - s42) -0.0030 +- 0.0006, median rel -0.006, share lower 0.668 (n=370) + n only (09-12T23-39-06Z) ep21-40 mean(loss - s42) -0.0012 +- 0.0004, median rel -0.003, share lower 0.612 (n=740) + n only (09-12T23-39-06Z) ep41-50 mean(loss - s42) -0.0016 +- 0.0005, median rel -0.002, share lower 0.584 (n=370) + +== B. reach-level parameter drift (KAN forward over all 346,321 CONUS reaches) + seed42 median n e10 0.0863 e25 0.0738 e50 0.0579; gamma e10 0.2333 e25 0.1513 e50 0.0665 + seed43 median n e50 0.0612; gamma e50 0.2722 (only the final field was kept) + median |ln n ratio|: seed42 e10->e25 0.176, e25->e50 0.225; seed42 vs seed43 at e50 0.337 + median |gamma change|: seed42 e10->e25 0.0815, e25->e50 0.0771; seed42 vs seed43 at e50 0.1978 + Spearman(n) seed42 vs seed43 at e50 0.490; Spearman(gamma) -0.120 + +== C. ensembles on the test years (equal-weight mean of routed predictions), paired against seed 42 (0.7391) + single 2026-09-10T21-21-48Z NSE median 0.7391 -> 0.7376 pop -0.0015 [-0.0042,+0.0012] paired +0.0001 up 51.9% down 48.1% + single 2026-09-10T21-21-48Z KGE median 0.7591 -> 0.7600 pop +0.0009 [-0.0017,+0.0032] paired -0.0000 up 49.0% down 51.0% + single 2026-09-11T06-38-49Z NSE median 0.7391 -> 0.7383 pop -0.0008 [-0.0033,+0.0030] paired -0.0001 up 47.9% down 52.1% + single 2026-09-11T06-38-49Z KGE median 0.7591 -> 0.7610 pop +0.0019 [-0.0009,+0.0045] paired -0.0002 up 46.0% down 54.0% + single 2026-09-12T03-53-34Z NSE median 0.7391 -> 0.7453 pop +0.0062 [+0.0035,+0.0103] paired +0.0006 up 57.3% down 42.7% + single 2026-09-12T03-53-34Z KGE median 0.7591 -> 0.7623 pop +0.0032 [+0.0010,+0.0057] paired +0.0002 up 54.8% down 45.2% + single 2026-09-12T06-06-19Z NSE median 0.7391 -> 0.7364 pop -0.0027 [-0.0051,+0.0004] paired -0.0001 up 46.7% down 53.3% + single 2026-09-12T06-06-19Z KGE median 0.7591 -> 0.7586 pop -0.0005 [-0.0036,+0.0017] paired -0.0003 up 42.0% down 58.0% + single 2026-09-12T16-30-14Z NSE median 0.7391 -> 0.7416 pop +0.0025 [+0.0002,+0.0063] paired +0.0002 up 52.9% down 47.1% + single 2026-09-12T16-30-14Z KGE median 0.7591 -> 0.7624 pop +0.0034 [+0.0004,+0.0059] paired -0.0002 up 45.8% down 54.2% + single 2026-09-12T20-36-00Z NSE median 0.7391 -> 0.7416 pop +0.0024 [+0.0002,+0.0060] paired +0.0002 up 53.4% down 46.6% + single 2026-09-12T20-36-00Z KGE median 0.7591 -> 0.7617 pop +0.0026 [-0.0001,+0.0047] paired -0.0001 up 47.1% down 52.9% + single 2026-09-12T20-38-08Z NSE median 0.7391 -> 0.7455 pop +0.0064 [+0.0037,+0.0099] paired +0.0007 up 58.0% down 42.0% + single 2026-09-12T20-38-08Z KGE median 0.7591 -> 0.7621 pop +0.0030 [+0.0010,+0.0059] paired +0.0000 up 50.9% down 49.1% + single 2026-09-12T23-39-06Z NSE median 0.7391 -> 0.7409 pop +0.0018 [-0.0005,+0.0049] paired +0.0004 up 58.0% down 42.0% + single 2026-09-12T23-39-06Z KGE median 0.7591 -> 0.7612 pop +0.0021 [+0.0000,+0.0043] paired +0.0002 up 57.2% down 42.8% + single 2026-09-27T07-29-47Z NSE median 0.7391 -> 0.7391 pop +0.0000 [+0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + single 2026-09-27T07-29-47Z KGE median 0.7591 -> 0.7591 pop +0.0000 [+0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + single 2026-09-27T10-31-30Z NSE median 0.7391 -> 0.7341 pop -0.0050 [-0.0074,-0.0018] paired -0.0007 up 40.2% down 59.8% + single 2026-09-27T10-31-30Z KGE median 0.7591 -> 0.7588 pop -0.0003 [-0.0018,+0.0030] paired -0.0002 up 44.6% down 55.4% + single 2026-09-13T17-22-30Z NSE median 0.7391 -> 0.7300 pop -0.0091 [-0.0138,-0.0039] paired -0.0062 up 41.5% down 58.5% + single 2026-09-13T17-22-30Z KGE median 0.7591 -> 0.7557 pop -0.0033 [-0.0091,+0.0018] paired -0.0067 up 44.1% down 55.9% + + ens two seeds of the working recipe (s42+s43) NSE median 0.7391 -> 0.7376 pop -0.0015 [-0.0036,+0.0009] paired -0.0001 up 46.4% down 53.6% + ens two seeds of the working recipe (s42+s43) KGE median 0.7591 -> 0.7607 pop +0.0016 [-0.0007,+0.0038] paired -0.0001 up 46.4% down 53.6% + mean of the members' own medians 0.7366; ensemble minus that +0.0010 + ens working recipe on two Q' stores (UH + dhbv2-dist aorc2f) NSE median 0.7391 -> 0.7430 pop +0.0039 [+0.0008,+0.0086] paired +0.0044 up 61.0% down 39.0% + ens working recipe on two Q' stores (UH + dhbv2-dist aorc2f) KGE median 0.7591 -> 0.7631 pop +0.0040 [-0.0001,+0.0079] paired +0.0009 up 51.8% down 48.2% + mean of the members' own medians 0.7346; ensemble minus that +0.0085 + ens two seeds + dhbv2-dist aorc2f (3 members) NSE median 0.7391 -> 0.7438 pop +0.0047 [+0.0015,+0.0088] paired +0.0048 up 65.8% down 34.2% + ens two seeds + dhbv2-dist aorc2f (3 members) KGE median 0.7591 -> 0.7646 pop +0.0055 [+0.0019,+0.0092] paired +0.0015 up 53.3% down 46.7% + mean of the members' own medians 0.7344; ensemble minus that +0.0094 + ens all 10 UH-store 50-epoch nse-batch variants NSE median 0.7391 -> 0.7436 pop +0.0045 [+0.0025,+0.0080] paired +0.0007 up 62.2% down 37.8% + ens all 10 UH-store 50-epoch nse-batch variants KGE median 0.7591 -> 0.7616 pop +0.0026 [+0.0006,+0.0052] paired +0.0000 up 50.4% down 49.6% + mean of the members' own medians 0.7400; ensemble minus that +0.0035 + ens UH variants without the three constant-gamma arms (7) NSE median 0.7391 -> 0.7431 pop +0.0040 [+0.0019,+0.0074] paired +0.0007 up 63.6% down 36.4% + ens UH variants without the three constant-gamma arms (7) KGE median 0.7591 -> 0.7617 pop +0.0026 [+0.0007,+0.0051] paired +0.0001 up 52.6% down 47.4% + mean of the members' own medians 0.7396; ensemble minus that +0.0035 + + per-gauge |dNSE| distribution: seed43-seed42 median |d| 0.0034, p90 0.0270; gamma0/n+p+q minus s42 median |d| 0.0047, p90 0.0368 + sign test gamma0/n+p+q vs s42: up 0.573; seed43 vs s42: up 0.402 + +== D. gauges within 0.03 below the seed-42 population median + n near = 174; in them seed43-seed42 median -0.0006, share down 0.575; |d| median 0.0043 (population |d| 0.0034) + crossings at the median: s42-below-and-near gauges that seed43 lifts above the s42 median: 17; s42-above gauges seed43 drops below: 54; s42-below that s43 lifts above: 18 + medians: population | near-median | near-median losing most (bottom quartile of seed43-seed42 there, n=44) + DRAIN_SQKM 871.675 | 534.460 | 609.292 + up_aridity 1.035 | 1.038 | 1.055 + up_snow_fraction 0.098 | 0.093 | 0.042 + tt_days_1ms 0.924 | 0.690 | 0.862 + longest_path_km 79.824 | 59.579 | 74.484 + n_reach 19.000 | 13.000 | 12.000 + nid_dor 0.009 | 0.001 | 0.002 + beta42 1.045 | 1.042 | 1.058 + alpha42 0.914 | 0.868 | 0.890 + r42 0.880 | 0.866 | 0.863 + near-median share Ref class 0.305 vs population 0.211; share with a NID dam >= 10 MCM 0.247 vs 0.388 + near-median HUC02 counts (top 6): 03:29, 07:21, 17:20, 02:19, 04:18, 18:13 + near-median losing-most HUC02 counts: 02:9, 07:7, 03:7, 05:4, 17:3, 04:3 + Spearman of |seed43-seed42 NSE| with attributes (population): + DRAIN_SQKM +0.174 + up_aridity +0.043 + up_snow_fraction -0.366 + tt_days_1ms +0.257 + n_reach +0.162 + nid_dor -0.038 + nse42 -0.080 + Spearman of signed (seed43-seed42) NSE with attributes (population): + DRAIN_SQKM -0.049 + up_aridity +0.014 + up_snow_fraction +0.009 + tt_days_1ms -0.062 + seed43-seed42 by drainage-area bin (median d, share down, n): + 0-300 -0.0004 0.578 491 + 300-1000 -0.0007 0.606 776 + 1000-3000 -0.0006 0.590 617 + 3000-10000 -0.0010 0.603 343 + 10000-10000000.0 -0.0025 0.645 138 diff --git a/experiments/levers/variance_by_gauge.csv b/experiments/levers/variance_by_gauge.csv new file mode 100644 index 00000000..f9733054 --- /dev/null +++ b/experiments/levers/variance_by_gauge.csv @@ -0,0 +1,2366 @@ +STAID,nse42,nse43,d43,nse_ens2,kge42,kge43,beta42,alpha42,r42,n_reach,total_length_km,mean_reach_length_km,DRAIN_SQKM,HUC02,AGGECOREGI,CLASS,STATE,LAT_GAGE,LNG_GAGE,obs_mean_q_m3s,up_SoilGrids1km_clay,up_aridity,up_meanelevation,up_meanP,up_NDVI,up_meanslope,up_log10_uparea,up_SoilGrids1km_sand,up_ETPOT_Hargr,up_Porosity,up_snow_fraction,up_snowfall_fraction,up_seasonality_P,up_seasonality_PET,up_meanTa,up_permeability,up_FW,up_glaciers,up_permafrost,out_log10_uparea,longest_path_km,gauge_slope,tt_days_1ms,n_nid_ge10mcm,nid_dor,nid_on_gauge_reach,nse_base,kge_base,near,n42,n43,g42,g43,dg,dlnn 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+04062000,0.2688443460779484,0.2733324591481813,0.0044881130702328,0.2711321123394952,0.3164433961336098,0.3193625827776496,0.7202424491713424,0.575278322706268,0.5432759692325101,37,317.3037327270508,8.575776560190562,1665.845,04,MxWdShld,Non-ref,MI,46.011064,-88.258468,5.145586967468262,10.646375116627889,1.087643775301412,476.3075724099351,778.4285877417612,0.5488494888190142,2.121171020188072,2.1276350145451675,57.005212065237146,845.7551092111203,0.0434445580226183,0.3692718421316044,0.2058582690285682,0.3500636681990855,0.7023384649213116,3.932596193376909,-14.050000190734863,0.0193638129060417,0.0,0.0,3.2223277148873306,111.92509838867188,0.0019840029999613,1.295429379498517,0,0.0243804245011764,True,0.2902272452431755,0.3434222201020386,False,0.06902392208576202,0.04872642457485199,0.07632043212652206,0.2889792323112488,0.21265880018472672,-0.34823165939904877 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+04063000,0.6013954985713266,0.6020338239671451,0.0006383253958185,0.6017744646570155,0.6317101809539314,0.6318675069152901,0.7768087553998254,0.7483719732342823,0.8499786838719168,103,899.8985569915772,8.736879194092982,4592.196,04,MxWdShld,Non-ref,MI,45.951064,-88.187077,48.991188049316406,11.123424535233502,1.0930082472060658,477.4402379099446,781.3198017076314,0.5430699361700835,2.353607406377735,2.17172580260546,56.32739517585181,852.3010366135155,0.0575261162676021,0.3502526059779668,0.2133516015820722,0.3336048238418081,0.7022866439432293,3.793987655821008,-14.073802723599488,0.0292152217392774,0.0,0.0,3.6667862638583495,162.95446801757814,0.0005707761738449,1.886047083536784,3,0.1514106092037822,False,0.7157496744787912,0.7403975093087398,False,0.06902392208576202,0.049113161861896515,0.07507707923650742,0.288590669631958,0.2135135903954506,-0.3403260801682822 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+04168000,0.6129443050290149,0.6091863528294382,-0.0037579521995766,0.6111123557536362,0.478477221566017,0.4770072206917679,0.5341297701537152,0.8108080477323599,0.8615899477435616,3,41.6420576171875,13.880685872395832,219.177,04,CntlPlains,Non-ref,MI,42.300593,-83.300208,2.67903995513916,23.64726439384733,1.1425441938888137,218.36088413461283,849.4294000117766,0.4454825723344219,1.3337173726650646,2.063857945908552,36.58594494828985,970.4411184254146,0.0372956851631777,0.1855141451520737,0.1420744450491263,0.150783185833587,0.611736603031918,9.256415895091688,-15.050000190734863,0.003746238747629,0.0,0.0,2.3539653131240423,34.9405107421875,0.0008041503024287,0.404404059516059,0,0.0,False,0.6380652186063986,0.5352817894579152,False,0.05254703015089035,0.05279236286878586,0.03676570579409599,0.2686304450035095,0.23186473920941353,0.004657956618803008 +04195500,0.7500950675939448,0.7484672637957064,-0.0016278037982384,0.7502228765703991,0.7664107944029348,0.7606380335570861,0.9128562158178511,0.8282639047629058,0.8678009400842852,27,294.5836633911133,10.910506051522717,1080.029,04,CntlPlains,Non-ref,OH,41.449496,-83.361316,10.92651653289795,27.429484536011767,1.1216141942170552,218.3583778314204,886.5796686189859,0.4145602369752083,0.7422993215419131,2.048231081868908,28.434768152678817,993.940173666024,0.1222264643684313,0.1924202251361077,0.1228750723985553,0.1824433708906895,0.5935510100552409,9.71033714006044,-15.049762616423148,0.0016955630246831,0.0,0.0,3.069512307647876,87.9351328125,0.0005885513382963,1.0177677408854169,0,0.0006054307397626,False,0.8520116568258036,0.8614654736219097,False,0.0424855574965477,0.05150074139237404,0.030055567622184753,0.2794613838195801,0.24940581619739532,0.19243200896789606 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+03136500,0.6627348055451888,0.6634868491196606,0.0007520435744717,0.6633114851309594,0.6844696573304536,0.6863746201515423,1.2456245063779687,1.1522841137567863,0.8733605602150313,13,107.65850317382812,8.281423321063702,520.659,05,NorthEast,Non-ref,OH,40.405618,-82.499885,6.491342544555664,23.5160514584408,0.9718179157933932,363.1013021228229,1034.909805389432,0.5218049839703622,2.02407347883154,1.9579000146880072,31.213608316534906,1005.7705831664456,0.2130231985781684,0.1274433476007171,0.1213342431261987,0.1623503318961476,0.5801068410986391,9.1488938084186,-15.050000190734863,0.0043050152643282,0.0,0.0,2.74304479324411,44.514489746093744,0.0013638344826176,0.5152140016908998,0,0.0258776612262274,False,0.6389082089928464,0.6597724692050054,False,0.04856918379664421,0.037544894963502884,0.04302675649523735,0.3167922794818878,0.27376552298665047,-0.2574518356036442 +03139000,0.7908322123115292,0.7886747637364755,-0.0021574485750537,0.7898124012711615,0.8297534964250931,0.8286613563076981,1.1401518872923166,1.013991105303051,0.9043675293622429,29,205.7019643554688,7.093171184671337,1202.849,05,NorthEast,Non-ref,OH,40.481454,-81.985979,12.877519607543944,21.45008683365984,0.9990483917421192,334.961757381514,991.19518121709,0.5599958906907399,3.280725707361301,2.121064565075713,37.49069088735618,990.142018257866,0.1903521874856879,0.108080709933932,0.1162605041536171,0.1817789814355097,0.5783715380088589,9.382791239022009,-15.050000190734863,0.0046339148002392,0.0,0.0,3.0944576071324983,92.39597503662108,0.0009238747297786,1.0693978592201514,0,0.0,False,0.7578055820733243,0.8121350814162596,False,0.05156316980719566,0.04621134698390961,0.0483788400888443,0.302649587392807,0.2542707473039627,-0.10958228056680927 +03144000,0.8030865117321151,0.8043350688158089,0.0012485570836937,0.8040362441834465,0.7863462305196129,0.7911415270524286,1.007785752416264,0.8110237343918678,0.9006255248602758,9,69.98062963867187,7.775625515407985,363.4146,05,EastHghlnds,Ref,OH,40.132568,-82.14792,4.418246746063232,23.090933381983834,1.008544377780341,306.2236160701508,1026.403810586258,0.5928162995901304,4.59500747116037,1.918534635745499,35.741875954968016,1035.1100290766449,0.1442883585616627,0.059948351921804,0.104821789768257,0.1499351658213353,0.5638998978993889,9.863538422993187,-15.050000190734863,0.0001045281462865,0.0,0.0,2.573004552717137,49.281500488281246,0.0006712629110552,0.5703877371328847,0,0.0,False,0.8193515843646919,0.8436247475854457,False,0.04960132762789726,0.04461580142378807,0.04903221130371094,0.3089548349380493,0.2599226236343384,-0.10592951177152893 +03145000,0.7788873389057074,0.7767909197630147,-0.0020964191426926,0.7780206124878026,0.8130552242521426,0.8094345065972696,1.0434511942894296,0.8606746819968872,0.8831719269286255,5,55.420948974609374,11.084189794921874,345.8997,05,CntlPlains,Non-ref,OH,39.988675,-82.474878,4.702748775482178,24.631962821009303,1.0226085655960595,302.8099544193281,1016.3116344199374,0.5088741337055568,1.5409434603633476,2.129346166752482,30.169060154922,1039.2754606322296,0.2177957384679544,0.1192305935178773,0.0963090193662696,0.1613431132531839,0.5555797469209505,10.251238556852169,-15.050000190734863,0.0222104951360447,0.0,0.0,2.548800022100267,44.226506103515625,0.0005072578205727,0.511880857679579,1,0.1153986171448825,False,0.7795660662931689,0.8261053345984366,False,0.04418880119919777,0.042477283626794815,0.03204819932579994,0.2904951572418213,0.25844695791602135,-0.039501960313113045 +03146500,0.8529670580752917,0.8550853720147957,0.002118313939504,0.854100923018809,0.814166826923628,0.8182565227028167,1.1663225941894162,1.052236841525045,0.935641043628544,25,283.64669079589845,11.345867631835938,1395.075,05,NorthEast,Non-ref,OH,40.059232,-82.339595,18.45155906677246,24.12027140754196,1.0155605912133294,316.3261296542248,1017.9948205010166,0.536322886902468,2.0227228808746744,2.1307338077652656,30.97381286649053,1033.6768854434983,0.1841593732736011,0.1114284059105266,0.1038289045516257,0.1583870482405416,0.5633327453666286,9.978258483066316,-15.050000190734863,0.0064495114419159,0.0,0.0,3.1632103158409186,72.40837377929688,0.0008482771809212,0.8380598817048249,1,0.0295344368777212,False,0.8401900501981369,0.7743358437565588,False,0.04313457012176514,0.04426515847444534,0.03570399805903435,0.2904951572418213,0.25479115918278694,0.025873110587540264 +03219500,0.7200327269529735,0.7448600109726942,0.0248272840197206,0.7351245461912235,0.8528331164337797,0.8563737357508782,0.95740621152861,0.9927330118022558,0.859319327252171,35,251.46523544311523,7.184721012660435,1469.508,05,CntlPlains,Non-ref,OH,40.419504,-83.197137,13.855243682861328,29.84021872085744,1.055511124057449,297.8015496609448,947.1524695332234,0.4234364570286058,0.9873140306302516,2.08892275480767,21.028748546986208,999.4866076520248,0.1873217097990595,0.1457280353259037,0.1155373616583309,0.1882664481534853,0.5777656311057446,9.856451594321944,-15.050000190734863,0.0024613322925829,0.0,0.0,3.161903495689772,99.89397631835936,0.0002721511991694,1.1561802814624926,0,0.0,False,0.5676538950739811,0.7880057807735054,True,0.03559061139822006,0.05873828008770943,0.024911649525165558,0.2506919205188751,0.22578027099370956,0.501009766947748 +03220000,0.7187093388626751,0.7181124158039297,-0.0005969230587453,0.7190211331415166,0.5712229147118357,0.5545594344720575,0.7493760843250145,0.6745646520170043,0.8769988778785414,3,71.09687890625,23.698959635416667,463.9194,05,CntlPlains,Non-ref,OH,40.248395,-83.173803,4.534120082855225,29.037719308139824,1.0656845268203334,316.7006022578523,969.4206476420532,0.4875088517837555,1.1692222317280496,2.368948132770034,23.230400983009343,1032.9442282933194,0.0865609947591675,0.137821857937259,0.1075291323734696,0.1885320599491463,0.5701252668442366,10.026615891921468,-15.050000190734862,0.0,0.0,0.0,2.5655665183586644,69.30264013671875,0.0010358580620959,0.8021138904712818,0,0.0,False,0.7269792581830337,0.628465825099837,True,0.03765776380896568,0.05092421919107437,0.02514275349676609,0.2535605728626251,0.22841781936585903,0.3017994864155387 +03221000,0.8607304826651654,0.8615120274613725,0.000781544796207,0.8622187482658356,0.8770408639868658,0.8573972009207563,0.941279696691833,0.9191438546112498,0.9283539333852402,53,491.0345875701904,9.264803539060196,2549.06,05,CntlPlains,Non-ref,OH,40.143396,-83.120466,25.74559783935547,29.613658235905344,1.0602940208820344,301.46939548933113,954.180942348359,0.4477074723973348,1.0119784189304175,2.190500653723092,21.620007281604344,1011.5860020745664,0.1625385075599958,0.1427822774591908,0.1123202952361832,0.1873407249478403,0.5746363961934693,9.928324457767786,-15.050000190734862,0.0015829196361112,0.0,0.0,3.401668206996757,137.91074316406247,0.0026961404364556,1.5961891569914637,1,0.0203623032738094,True,0.7008562916459742,0.8489021332954274,False,0.03628982603549957,0.06117232143878937,0.024911649525165558,0.24632257223129272,0.22141092270612717,0.5221573956615476 +03225500,0.3205458536420241,0.3258358437134021,0.0052899900713779,0.3399102932424402,0.5884729021956228,0.6106409398960805,0.8790188106509852,0.8812384739988026,0.6250149283066615,21,271.0024414672852,12.904878165108816,1019.562,05,CntlPlains,Non-ref,OH,40.355061,-83.067132,10.625041961669922,25.9675368836602,1.0284216967332525,305.5213715796897,987.1526892156726,0.479778534694034,1.1093310781228007,2.396926917736905,26.626641277088023,1014.4596448275746,0.1467938326347928,0.1251880568383284,0.1110136975822367,0.1829964632374563,0.576571621842422,9.76368661150176,-15.050000190734863,0.0097687320110302,0.0,0.0,3.145343004473617,144.76220178222655,0.0009865316096693,1.6754884465535482,1,0.0481137331202186,True,-0.0569222632597485,0.3611160713913284,False,0.043901123106479645,0.05227411538362503,0.03043438494205475,0.2651154398918152,0.23468105494976044,0.17456141976641146 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+03228500,0.67856231388953,0.6652015768122332,-0.0133607370772967,0.6730997244282655,0.7631359420733924,0.756500027525387,1.0101726112755387,0.8418472914563955,0.8239635813315387,13,100.0602999572754,7.696946150559645,490.9968,05,CntlPlains,Non-ref,OH,40.103674,-82.884068,6.409129142761231,24.01739695609627,1.009849457566394,326.9011636112524,1016.8140325260404,0.5114366147660843,1.365173574993742,1.99371807500798,30.73465927325568,1026.362838026221,0.1532194216293086,0.1206070936222902,0.1069583455085557,0.1693850252314863,0.569066636823124,9.86656057147946,-15.050000190734863,0.0117988531400131,0.0,0.0,2.7335435405346136,65.34828686523437,0.0017328994581475,0.7563459127920644,1,0.4499852751126348,True,0.6189695848541719,0.7836806378273782,False,0.052531469613313675,0.04791984334588051,0.04015938565135002,0.28984981775283813,0.2496904321014881,-0.09188272649652013 +03228805,-0.1009791929570504,-0.1549228345477713,-0.0539436415907208,-0.1197139455803726,0.3228446886699687,0.2975964499354177,1.2367134685975376,0.8647935372351194,0.3801409338618954,5,101.01867053222657,20.20373410644532,325.5012,05,CntlPlains,Non-ref,OH,40.182285,-82.961571,2.5226645469665527,24.228928019443384,1.0397966910908802,279.7852923586494,991.3770406167648,0.4917694859323027,1.2068158528298227,2.3257409137453746,30.941293056114112,1030.7043075894044,0.1781497555259201,0.0966229278639437,0.1024570876724823,0.1768949951713254,0.5667274973906549,10.193140190874155,-15.050000190734863,0.0304983561721124,0.0,0.0,2.695793198877205,85.076361328125,0.0009566053049638,0.9846801079644096,1,1.2332582086618709,True,-0.7571453091724898,-0.2595700255475237,False,0.05401042476296425,0.04613379389047623,0.035657864063978195,0.278817743062973,0.24315987899899483,-0.15763134145760993 +04196800,0.8031182082701276,0.7867628675139022,-0.0163553407562253,0.7979459678394649,0.8207068552717085,0.8039036528496248,1.044027499745947,0.8598933525194588,0.8971518675694885,15,127.2343967590332,8.48229311726888,608.067,04,CntlPlains,Ref,OH,40.922833,-83.348812,4.998688697814941,29.712513700221315,1.0496377631233726,274.4648324385721,936.7881164002904,0.4091328418617575,1.133363363053569,2.03481267970982,21.43227117701782,983.2750263456865,0.1427989054261186,0.1609838951067851,0.1184841175499514,0.210844977050838,0.5819893468351567,9.908749921969594,-15.050000190734858,0.0060291007185379,0.0,0.0,2.801028506002157,77.14545486450194,0.0009945123456418,0.8928872090798835,0,0.0,False,0.8054470421225877,0.8436802533362854,False,0.036349572241306305,0.05829782411456108,0.02236352488398552,0.24354448914527893,0.2211809642612934,0.4723823343064391 +04197100,0.7198074737151957,0.7086844208567353,-0.0111230528584603,0.714522370150236,0.7243819860137288,0.72131396661258,0.9417670966831028,0.7746899872157098,0.8523192065833735,7,96.42651879882813,13.77521697126116,388.2105,04,CntlPlains,Ref,OH,41.022278,-83.109638,3.8816301822662354,27.39285396188747,1.01003009018491,286.33552971968084,991.5017042249935,0.4436499041982967,1.2110292817327424,2.15130020897254,23.74498135834914,1000.5719671312144,0.1161765906520208,0.1707464918265987,0.1229853639795472,0.1757442134332353,0.5879321193134435,9.546572965941612,-15.050000190734863,0.0011571286055116,0.0,0.0,2.653068091329235,74.62449853515625,0.0014323573559522,0.8637094737865306,0,0.0,False,0.782152659805755,0.8394802156844189,True,0.0377214290201664,0.06122509390115738,0.028652315959334373,0.2559879422187805,0.22733562625944614,0.48432879454945105 +04198000,0.8342216138814271,0.8330173284290588,-0.0012042854523682,0.8340605934579731,0.7922899278997664,0.7862966247158483,0.9192284056642702,0.8266531395569403,0.9189426772285012,71,739.0364916381836,10.408964670960334,3243.828,04,CntlPlains,Non-ref,OH,41.307831,-83.158809,31.91657829284668,28.505754332278546,1.0385410106731596,271.0051636153433,960.604316547884,0.4276115683102939,1.120841393957911,2.145521945794968,23.34348189046351,996.695156530366,0.1310180309338897,0.1688717371426671,0.1207936994303977,0.1900752451406872,0.5866611968198617,9.727478158195815,-15.050000190734863,0.0024980559208552,0.0,0.0,3.5152399724165027,206.07145373535155,0.0010307789780199,2.3850862700850874,0,0.0005732712349264,False,0.802245447524787,0.8749510811381478,False,0.037470266222953796,0.06013664975762367,0.02582353912293911,0.24847431480884552,0.2226507756859064,0.47307175087654374 +04199000,0.730667435004027,0.7390017856199456,0.0083343506159185,0.7353045304616961,0.6748495405080431,0.6859452600921272,0.9012054405510588,0.7193626862937251,0.8688315796891412,21,199.2678445739746,9.488944979713075,947.3742,04,CntlPlains,Non-ref,OH,41.301719,-82.606844,10.886150360107422,24.5542010979342,0.9959306988632766,264.3083716841472,986.080279168424,0.4743358439141437,1.095004191669507,2.0162560894642807,29.09323661430164,981.6008553745394,0.1066923608286303,0.1735499653460474,0.1190829818466183,0.1670482705414993,0.5851617055757948,9.531588908482922,-15.049728548972272,0.0151970615496901,0.0,0.0,2.971030414954537,92.42422558593748,0.0003959633759222,1.0697248331705729,0,0.0098123176591202,False,0.7760627130813664,0.7594717982922838,True,0.046199411153793335,0.048909980803728104,0.03407548367977142,0.2750093638896942,0.2409338802099228,0.057014429652074886 +03157500,0.8470025703601332,0.8485281948537026,0.0015256244935694,0.8480299941377073,0.8355269376303474,0.8413992654933491,1.112952450785679,1.1019938070324444,0.937626965491577,25,194.32535400390623,7.77301416015625,1194.712,05,CntlPlains,Non-ref,OH,39.565066,-82.474601,14.188505172729492,24.288528551503344,1.0679560591043435,289.43150388121364,1000.3977044364618,0.5645216368615927,3.0007058036346863,2.1332845108994687,32.57930417422907,1067.9371548864403,0.1150562261033905,0.0790409541758293,0.0885074437989994,0.1544910830262659,0.5504917000580907,10.42908708178335,-15.050000190734863,0.0080871534589601,0.0,0.0,3.084255424593133,74.95336267089844,0.0005267559317871,0.8675157716539171,0,0.0010408026841746,False,0.807989249832785,0.7615805231741362,False,0.03911246731877327,0.04815370962023735,0.030009090900421143,0.2805659770965576,0.2505568861961365,0.2079569048472824 +03159500,0.8591135310563675,0.853265847584106,-0.0058476834722615,0.8565070463613758,0.8952956576491251,0.8970531158086026,1.0746297398938094,1.0306525678426126,0.9332630438575028,51,445.4642679443359,8.734593489104626,2447.092,05,EastHghlnds,Non-ref,OH,39.328962,-82.087644,31.5404052734375,23.919920940787826,1.0725007559711268,280.2891627058588,1007.390079580164,0.5886052822012742,4.354387476865239,2.1861552714314323,34.85333847740172,1080.1359967169185,0.1193176257778515,0.0509497582784037,0.0851663338287696,0.1479933302000901,0.54503151017544,10.550071754865474,-15.050000190734869,0.0069804796197872,0.0,0.0,3.395875897758832,148.58670275878904,0.0002177134010707,1.7197535041526508,1,0.0163744288603453,False,0.7192193657927992,0.7872844975676058,False,0.039959702640771866,0.048474036157131195,0.033950917422771454,0.29048389196395874,0.2565329745411873,0.19315680530253626 +03229500,0.7549603238357474,0.7789750936098898,0.0240147697741424,0.7698379118652657,0.771804273901121,0.7976656257481608,0.9945262746127066,0.8119208385273925,0.8708893623285057,26,304.9044431762695,11.72709396831806,1424.002,05,CntlPlains,Non-ref,OH,39.85673,-82.957126,12.97347927093506,24.188619446249152,1.0273834207626134,296.6718416385193,1003.2335563561458,0.5002211235756949,1.274966049814673,2.174958291969112,31.17010476614543,1030.2784175275017,0.1585951757094066,0.1034811128983831,0.1013334651230111,0.1730068567063475,0.5648638129201796,10.17198838351691,-15.050000190734863,0.0172743732662888,0.0,0.0,3.155078047374776,113.0888220214844,0.0005701748887076,1.3088984030264397,2,0.4290569423142032,False,0.7632920708255606,0.7822896185527404,False,0.0531717911362648,0.04820212349295616,0.03740951418876648,0.2796590030193329,0.2422494888305664,-0.09812493796358547 +03230500,0.813289072420375,0.8125895853615731,-0.0006994870588018,0.8137047499954988,0.8257786712200563,0.8226303992601969,1.061998391040191,0.8692287735727241,0.903004344174685,27,306.4439091796875,11.349774414062502,1376.946,05,CntlPlains,Non-ref,OH,39.700618,-83.110187,14.691679954528809,29.941013411449124,1.0731342994189474,298.0741350564316,982.417769224814,0.4573275502756919,1.024350950929636,2.2580832665728607,21.55960027081206,1054.1455173965403,0.1458003544442347,0.1369953434312298,0.0967379848700267,0.1896797241743021,0.5645249895743427,10.360867417081826,-15.050000190734863,0.0003605402786223,0.0,0.0,3.1617060098042886,140.0212957763672,0.0008755208691582,1.6206168492635091,0,0.0,False,0.8053211814013619,0.8034821087091858,False,0.03679652884602547,0.062070801854133606,0.02220412902534008,0.24406033754348755,0.22185620851814747,0.5228721829980963 +03231500,0.8406923009786437,0.8445671267879469,0.0038748258093032,0.850995374593058,0.8330752474129535,0.8478015453231174,0.8746405971759903,0.9223374760873622,0.9217860272753434,196,1963.9162637786865,10.01998093764636,9971.048,05,CntlPlains,Non-ref,OH,39.341453,-82.971013,112.1144027709961,27.82964211351945,1.0560942216130909,286.98564812781075,980.840167975612,0.4701733938233156,1.124670743302132,2.290395502429253,24.803548100350323,1035.5110009965708,0.148783507074888,0.1264535435757658,0.0996646972073796,0.1801615755933599,0.5651679397161894,10.28412799516749,-15.050000190734863,0.0063955441723225,0.0,0.0,3.99843796801734,280.4385693359375,0.0004971002344973,3.245816774721499,5,0.0700836598583901,False,0.5203773554031226,0.7617785704167636,False,0.041333600878715515,0.05843161977827549,0.0286830747500062,0.250084713101387,0.22140163835138083,0.34618142808276015 +03232500,0.7504177551108713,0.7499634789893223,-0.000454276121549,0.750430871674451,0.8483142449027916,0.84814539197917,1.077262691358827,0.9712705182340536,0.8726671224668936,5,57.62402221679688,11.524804443359375,363.2454,05,CntlPlains,Non-ref,OH,39.218399,-83.38547,4.076022148132324,27.98691364609914,0.990045769833524,309.77000081808967,1062.1349816366128,0.5627371746597213,1.8934021466232456,2.1691101765281755,26.857135326705087,1051.457388250148,0.1682392069205306,0.0825222571067965,0.0851903953272374,0.1479329453901831,0.5378790553678027,11.248218489966929,-15.050000190734863,0.0212760064453301,0.0,0.0,2.5746921419714304,40.9035419921875,0.0018562445184215,0.4734206249095775,1,0.3303158750215714,True,0.7320893257467731,0.8121545276571208,False,0.03244231268763542,0.050363101065158844,0.02460794523358345,0.2660609185695648,0.24145297333598137,0.4397952672820469 +03234300,0.6998604400176679,0.7069868183203963,0.0071263783027284,0.7035540821651879,0.8140373510753384,0.8127483330994623,0.9889523046063518,0.9070017399035604,0.8393407990789166,59,633.9064674682617,10.7441774147163,2944.457,05,CntlPlains,Non-ref,OH,39.320342,-82.978235,40.97558975219727,29.538265493872185,1.026250650946683,295.03466047526257,1024.0886341425228,0.4909678761758445,1.7721119943335495,2.2084245213733764,22.63298205499262,1050.4946126148852,0.144449896096095,0.1116437492661642,0.0862085236707692,0.1585519178795726,0.5449033932107884,11.037202254849142,-15.050000190734863,0.0045862769286798,0.0,0.0,3.473451969566669,179.43342980957024,0.0007277028635144,2.076775807981137,2,0.0565849633246189,False,0.6833831173032101,0.839321476351978,False,0.03551935777068138,0.060289572924375534,0.024004438892006874,0.24260088801383972,0.21859644912183285,0.5290813316068381 +03234500,0.8481684747887243,0.8450343692423428,-0.0031341055463814,0.8515868584088813,0.892137774241826,0.8987902638097863,0.9305288105377328,0.9685942112948264,0.9236999998950784,266,2666.6457406463624,10.02498398739234,13284.22,05,CntlPlains,Non-ref,OH,39.212289,-82.863785,150.39486694335938,28.150993937747465,1.0501285880937754,288.0826196928156,991.3340743980344,0.4777745794550477,1.3582674074289804,2.268056633867193,24.453537624491663,1040.4561418902329,0.1480636740163937,0.1208362938288429,0.0961420666651652,0.1744752968450778,0.5599965624753945,10.472784821539667,-15.050000190734863,0.0059100018180054,0.0,0.0,4.122587893264477,303.3332800292969,0.0004039878840558,3.510801852190936,7,0.0668964599602938,False,0.5563585681182739,0.7599996527247915,False,0.040414342656731606,0.05925041064620018,0.027623798698186874,0.24903492629528046,0.22141112759709358,0.3825879725093016 +03237500,0.7452862899939323,0.740607131889413,-0.0046791581045193,0.7430269459023251,0.7548527167313002,0.7493536707285845,0.964182193034108,0.7978093777448596,0.8660849990842795,27,198.39546740722656,7.347980274341724,1003.208,05,CntlPlains,Ref,OH,38.803684,-83.421023,15.091041564941406,26.27689116299773,1.0129056343693357,271.518560167985,1082.9384478353963,0.5848861517017481,3.4349832801421294,2.033432004296694,30.15968209254596,1096.8137394404412,0.1621112535066917,0.0564700168590885,0.0770007055869103,0.1418396702165197,0.5285908697083522,11.525225482355888,-15.050000190734863,0.0061340257354138,0.0,0.0,3.019995756040675,90.2388311767578,9.912940731737764e-05,1.04443091639766,0,0.0,False,0.7572056792312327,0.8416701681601693,False,0.04118766263127327,0.04134112223982811,0.02995452843606472,0.29688340425491333,0.2669288758188486,0.0037189396563448796 +03238500,0.7494250394081059,0.7362709232025931,-0.0131541162055127,0.7441222904915248,0.7509205618528854,0.7412577235140109,0.9759845968697934,0.7892471687904493,0.8694355674108197,9,125.2629130859375,13.918101453993057,568.5038,05,CntlPlains,Ref,OH,38.858125,-83.928539,9.18589687347412,27.826510014424965,0.9783174983234744,296.7479100558911,1093.5066035289535,0.5194920682901567,1.59399141683266,2.2139976837931927,27.09401654335284,1069.8386985072991,0.1142669063251011,0.1176606335177293,0.080478635887844,0.153765545486179,0.5311988313661611,11.295129365507965,-15.050000190734869,0.0019538214078515,0.0,0.0,2.782473236137503,75.86676171875,0.0032299559097737,0.8780875198929398,0,0.0059575248879789,False,0.7696912575373316,0.8461318509404578,False,0.04279354214668274,0.059433165937662125,0.027518751099705696,0.24483850598335266,0.21731975488364697,0.32846521288836833 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00000000..ec198aa7 --- /dev/null +++ b/experiments/levers/variance_gamma.py @@ -0,0 +1,62 @@ +"""Is the seed-to-seed skill difference the unidentified gamma wandering? Per gauge: basin-median (over the gauge's +upstream reaches) n and gamma for seed 42 and seed 43 (final KAN fields), against the per-gauge test NSE difference. +Also: the constant-gamma sweep (p, q learned, seed 42) as an external check of skill against gamma. +Writes variance_gamma.txt. +""" +import numpy as np +import pandas as pd +import xarray as xr +import zarr +from scipy import stats + +import common as C + +out = [] +say = out.append +V = pd.read_csv(C.HERE / "variance_by_gauge.csv", dtype={"Unnamed: 0": str}).rename(columns={"Unnamed: 0": "STAID"}) +V = V.set_index("STAID") +gz = zarr.open_group("/home/tbindas/projects/ddr/data/merit_gages_conus_adjacency.zarr", mode="r") + + +def field(run, f="kan_parameters.nc"): + ds = xr.open_dataset(C.RUNS / run / "plot" / f) + return pd.DataFrame(dict(n=ds.n.values.astype(float), gamma=ds.gamma.values.astype(float)), index=ds.COMID.values) + + +F42, F43 = field(C.S42, "kan_parameters_epoch50.nc"), field(C.S43) +rows = [] +for s in V.index: + order = np.asarray(gz[s]["order"][:], dtype=np.int64) + a, b = F42.reindex(order), F43.reindex(order) + rows.append((s, a.n.median(), b.n.median(), a.gamma.median(), b.gamma.median())) +B = pd.DataFrame(rows, columns=["STAID", "n42", "n43", "g42", "g43"]).set_index("STAID") +V = V.join(B) +V["dg"] = V.g43 - V.g42 +V["dlnn"] = np.log(V.n43 / V.n42) +say(f"basin-median gamma: seed42 median {V.g42.median():.3f}, seed43 {V.g43.median():.3f}; share of gauges where seed43 " + f"gamma is higher {np.mean(V.dg > 0):.3f}") +say(f"basin-median n: seed42 {V.n42.median():.4f}, seed43 {V.n43.median():.4f}") +for c in ["dg", "dlnn", "g43", "g42"]: + ok = V[c].notna() & V.d43.notna() + say(f"Spearman(seed43 - seed42 NSE, {c}) = {stats.spearmanr(V.d43[ok], V[c][ok]).correlation:+.3f} (n={ok.sum()})") +say("seed43 - seed42 NSE by tercile of the basin gamma difference (median d, share down):") +q = pd.qcut(V.dg, 3, labels=["low", "mid", "high"]) +for k in ["low", "mid", "high"]: + m = q == k + say(f" {k:4s} dg median {V.dg[m].median():+.3f}: d NSE median {V.d43[m].median():+.4f}, share down {np.mean(V.d43[m] < 0):.3f}") +say("by area bin: median basin gamma s42 | s43, and d NSE:") +for lo, hi in [(0, 300), (300, 1000), (1000, 3000), (3000, 10000), (10000, 1e7)]: + m = (V.DRAIN_SQKM >= lo) & (V.DRAIN_SQKM < hi) + say(f" {lo:>6}-{hi:<9} {V.g42[m].median():.3f} | {V.g43[m].median():.3f} {V.d43[m].median():+.4f}") +# external check: the constant-gamma sweep (seed 42, n+p+q learned), population median NSE vs gamma +sweep = {0.0: "2026-09-12T03-53-34Z-train-and-test", 0.1: "2026-09-12T20-38-08Z-train-and-test", + 0.183: "2026-09-12T20-36-00Z-train-and-test", 0.35: "2026-09-12T06-06-19Z-train-and-test"} +ids, t, P, O = C.load_preds(C.S42) +say("constant-gamma sweep (seed 42, n+p+q learned): gamma -> population median NSE / KGE") +for g, r in sweep.items(): + i, tt, Pr, _ = C.load_preds(r) + M = C.metrics(C.align(i, Pr, ids), O) + say(f" {g:5.3f}: {np.nanmedian(M.nse):.4f} / {np.nanmedian(M.kge):.4f}") +V.to_csv(C.HERE / "variance_by_gauge.csv") +open(C.HERE / "variance_gamma.txt", "w").write("\n".join(out) + "\n") +print("\n".join(out)) diff --git a/experiments/levers/variance_gamma.txt b/experiments/levers/variance_gamma.txt new file mode 100644 index 00000000..b6369ff8 --- /dev/null +++ b/experiments/levers/variance_gamma.txt @@ -0,0 +1,21 @@ +basin-median gamma: seed42 median 0.071, seed43 0.273; share of gauges where seed43 gamma is higher 0.929 +basin-median n: seed42 0.0651, seed43 0.0599 +Spearman(seed43 - seed42 NSE, dg) = -0.107 (n=2365) +Spearman(seed43 - seed42 NSE, dlnn) = -0.067 (n=2365) +Spearman(seed43 - seed42 NSE, g43) = -0.042 (n=2365) +Spearman(seed43 - seed42 NSE, g42) = +0.115 (n=2365) +seed43 - seed42 NSE by tercile of the basin gamma difference (median d, share down): + low dg median +0.067: d NSE median -0.0003, share down 0.593 + mid dg median +0.207: d NSE median -0.0007, share down 0.582 + high dg median +0.243: d NSE median -0.0017, share down 0.618 +by area bin: median basin gamma s42 | s43, and d NSE: + 0-300 0.087 | 0.270 -0.0004 + 300-1000 0.071 | 0.272 -0.0007 + 1000-3000 0.067 | 0.275 -0.0006 + 3000-10000 0.066 | 0.274 -0.0010 + 10000-10000000.0 0.065 | 0.275 -0.0025 +constant-gamma sweep (seed 42, n+p+q learned): gamma -> population median NSE / KGE + 0.000: 0.7453 / 0.7623 + 0.100: 0.7455 / 0.7621 + 0.183: 0.7416 / 0.7617 + 0.350: 0.7364 / 0.7586 diff --git a/experiments/levers/volume.py b/experiments/levers/volume.py new file mode 100644 index 00000000..39b869e2 --- /dev/null +++ b/experiments/levers/volume.py @@ -0,0 +1,194 @@ +"""Lever 1: runoff volume multipliers, fitted on TRAINING years (WY1982-1995), scored on TEST years (WY1996-2010). + +Usage: volume.py + training-year predictions: the zero-step replay of the seed's no-dam checkpoint (testing window 1981-10-01..1996-09-30); + test-year predictions: the source run's own test phase (exactly what the run scored). +Checks the replay against the source run on the WY1996 overlap first. + +Multipliers are applied to the routed discharge (post hoc). In the engine a Q' multiplier would also shift celerity a +little (routing is nonlinear in Q through depth), so these are first-order estimates. + +Writes volume_s.txt / .json and volume_by_gauge_s.csv. +""" +import json +import sys + +import numpy as np +import pandas as pd +from sklearn.ensemble import HistGradientBoostingRegressor +from sklearn.linear_model import RidgeCV +from sklearn.model_selection import GroupKFold, KFold +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler + +import common as C + +seed, replay = int(sys.argv[1]), sys.argv[2] +src = {42: C.S42, 43: C.S43}[seed] +out, res = [], {} +say = out.append + +ids, tt, PT, OT = C.load_preds(src) +rid, tr, PR, OR = C.load_preds(replay) +PR, OR = C.align(rid, PR, ids), C.align(rid, OR, ids) +wy_t, wy_r = C.water_year(tt), C.water_year(tr) + +# ---- replay check on the WY1996 overlap (skip October 1995: the source run cold-started on 1995-10-01) ---- +ov = pd.Timestamp("1995-11-01") +tpos = {d: k for k, d in enumerate(tr)} +i_t = np.array([k for k in np.where((wy_t == 1996) & (tt >= ov))[0] if tt[k] in tpos]) +i_r = np.array([tpos[tt[k]] for k in i_t]) +a, b = PT[:, i_t], PR[:, i_r] +rel = np.nanmax(np.abs(a - b) / np.maximum(np.abs(a), 1e-3), axis=1) +nse_ov_t = C.metrics(a, OT[:, i_t], min_days=200).nse +nse_ov_r = C.metrics(b, OR[:, i_r], min_days=200).nse +say(f"== seed {seed}: source {src}, replay {replay}") +say(f"replay check, WY1996 from 1995-11-01 ({len(i_t)} days): per-gauge max relative difference median {np.nanmedian(rel):.2e}, " + f"p99 {np.nanpercentile(rel, 99):.2e}, max {np.nanmax(rel):.2e}; |NSE diff| max {np.nanmax(np.abs(nse_ov_t - nse_ov_r)):.2e}; " + f"obs identical {np.array_equal(np.nan_to_num(OT[:, i_t], nan=-9), np.nan_to_num(OR[:, i_r], nan=-9))}") +res["replay_check"] = dict(rel_median=float(np.nanmedian(rel)), rel_p99=float(np.nanpercentile(rel, 99)), + rel_max=float(np.nanmax(rel)), nse_absdiff_max=float(np.nanmax(np.abs(nse_ov_t - nse_ov_r)))) + +# ---- windows ---- +import os +TWY = [int(x) for x in os.environ.get("LEVERS_TRAIN_WY", "1982,1995").split(",")] # smoke-test override only +trn = (wy_r >= TWY[0]) & (wy_r <= TWY[1]) & (tr >= pd.Timestamp("1981-11-01")) # drop the replay's cold-start month +tst = (wy_t >= 1996) & (wy_t <= 2010) # the source run's whole test phase +Ptr, Otr = PR[:, trn], OR[:, trn] +Pte, Ote = PT[:, tst], OT[:, tst] +say(f"training window {tr[trn][0].date()}..{tr[trn][-1].date()} ({trn.sum()} d); test {tt[tst][0].date()}..{tt[tst][-1].date()} " + f"({tst.sum()} d)") + +Mtr = C.metrics(Ptr, Otr) +Mte = C.metrics(Pte, Ote) +base_n, base_k = Mte.nse.values, Mte.kge.values +BASE_N, BASE_K = np.nanmedian(base_n), np.nanmedian(base_k) +say(f"test-year median NSE {BASE_N:.4f} KGE {BASE_K:.4f} (n={np.isfinite(base_n).sum()}); training-year median NSE " + f"{np.nanmedian(Mtr.nse):.4f} KGE {np.nanmedian(Mtr.kge):.4f} (n={np.isfinite(Mtr.nse).sum()} with >= 365 valid days)") + +ktr = C.ls_scale(Ptr, Otr) # NSE-optimal per-gauge scalar, training years +vtr = C.vol_ratio(Ptr, Otr) # observed / predicted volume, training years +kte = C.ls_scale(Pte, Ote) +vte = C.vol_ratio(Pte, Ote) +ok_tr = np.isfinite(Mtr.nse.values) & np.isfinite(ktr) & (ktr > 0) +say(f"predicted/observed volume (1/v): training median {np.nanmedian(1 / vtr[ok_tr]):.4f} (share > 1: " + f"{np.mean(1 / vtr[ok_tr] > 1):.3f}); test median {np.nanmedian(1 / vte):.4f} (share > 1: {np.nanmean(1 / vte > 1):.3f})") +say(f"per-gauge LS scalar k*: training median {np.nanmedian(ktr[ok_tr]):.4f}, test median {np.nanmedian(kte):.4f}; " + f"Spearman(train k*, test k*) {pd.Series(ktr).corr(pd.Series(kte), method='spearman'):.3f}; " + f"Spearman(train vol ratio, test vol ratio) {pd.Series(vtr).corr(pd.Series(vte), method='spearman'):.3f}") + +G = pd.read_csv(C.HERE / "gauges_table.csv", dtype={"STAID": str, "HUC02": str}).set_index("STAID").reindex(ids) +huc = G.HUC02.values + + +def score(mult, name): + """Apply per-gauge multipliers (array or scalar) to test-year predictions; summarize NSE and KGE.""" + m = np.broadcast_to(np.asarray(mult, dtype=float), (len(ids),)).copy() + m[~np.isfinite(m)] = 1.0 + M = C.metrics(Pte * m[:, None], Ote) + s = C.summarize(name, M.nse.values, base_n) + sk = C.summarize(name, M.kge.values, base_k, "KGE") + say(C.fmt(s)) + say(C.fmt(sk)) + res[name] = dict(nse=s, kge=sk, mult_median=float(np.median(m)), mult_p10=float(np.percentile(m, 10)), + mult_p90=float(np.percentile(m, 90))) + return M + + +GRID = np.round(np.arange(0.80, 1.1001, 0.0025), 4) + + +def best_median_scalar(P, O, rows): + """Scalar maximizing the median training-year NSE over `rows` (grid).""" + best, arg = -np.inf, 1.0 + for k in GRID: + v = np.nanmedian(C.metrics(P[rows] * k, O[rows]).nse.values) + if v > best: + best, arg = v, k + return arg, best + + +say("\n-- global scalars (fit on training years)") +k_med, v_med = best_median_scalar(Ptr, Otr, np.arange(len(ids))) +say(f" scalar maximizing training median NSE: {k_med} (training median {v_med:.4f} vs {np.nanmedian(Mtr.nse):.4f})") +score(k_med, f"global scalar {k_med} (max training median NSE)") +k_ls = float(np.nanmedian(ktr[ok_tr])) +score(k_ls, f"global scalar {k_ls:.4f} (median per-gauge LS k*)") +k_v = float(np.nanmedian(vtr[ok_tr])) +score(k_v, f"global scalar {k_v:.4f} (median volume ratio)") +score(0.95, "fixed 0.95 (reference; chosen on test years earlier)") + +say("\n-- per-HUC2 scalars (fit on training years within each HUC2)") +m_h_med, m_h_ls, m_h_v = np.ones(len(ids)), np.ones(len(ids)), np.ones(len(ids)) +hk = {} +for h in sorted(set(huc)): + rows = np.where(huc == h)[0] + kk, _ = best_median_scalar(Ptr, Otr, rows) + r_ok = rows[ok_tr[rows]] + m_h_med[rows] = kk + m_h_ls[rows] = np.nanmedian(ktr[r_ok]) if len(r_ok) else 1.0 + m_h_v[rows] = np.nanmedian(vtr[r_ok]) if len(r_ok) else 1.0 + hk[h] = dict(n=len(rows), k_med=float(kk), k_ls=float(m_h_ls[rows][0]), k_v=float(m_h_v[rows][0])) +say(" " + " ".join(f"{h}:{v['k_ls']:.3f}(n{v['n']})" for h, v in hk.items())) +res["huc2_scalars"] = hk +score(m_h_med, "per-HUC2 scalar (max training median NSE in the HUC2)") +score(m_h_ls, "per-HUC2 scalar (median per-gauge LS k* in the HUC2)") +score(m_h_v, "per-HUC2 scalar (median volume ratio in the HUC2)") + +say("\n-- attribute-conditioned multiplier: predict log training k* from catchment attributes, out-of-fold by gauge") +FEAT = ["up_SoilGrids1km_clay", "up_aridity", "up_meanelevation", "up_meanP", "up_NDVI", "up_meanslope", "out_log10_uparea", + "up_SoilGrids1km_sand", "up_ETPOT_Hargr", "up_Porosity", "up_snow_fraction", "up_snowfall_fraction", + "up_seasonality_P", "up_meanTa", "up_permeability", "LAT_GAGE", "LNG_GAGE"] +X = G[FEAT].values.astype(float) +X = np.where(np.isfinite(X), X, np.nanmedian(X, axis=0)) +fit_ok = ok_tr & np.isfinite(ktr) & (ktr > 0.2) & (ktr < 5) +say(f" gauges usable for fitting: {fit_ok.sum()} (training NSE finite, 0.2 < k* < 5); features: {len(FEAT)}") + + +def oof(target, model_fn, splitter, groups=None, clip=(0.7, 1.3)): + """Out-of-fold predictions for every gauge; fit rows exclude the held-out fold. Gauges not usable for fitting still + receive a prediction from the fold that holds them out.""" + y = np.log(target) + pred = np.full(len(ids), np.nan) + idx = np.arange(len(ids)) + for tr_i, te_i in splitter.split(idx, groups=groups): + fit = tr_i[fit_ok[tr_i]] + mdl = model_fn() + mdl.fit(X[fit], y[fit]) + pred[te_i] = mdl.predict(X[te_i]) + r2 = 1 - np.nanmean((pred[fit_ok] - y[fit_ok]) ** 2) / np.nanvar(y[fit_ok]) + return np.clip(np.exp(pred), *clip), r2 + + +ridge = lambda: make_pipeline(StandardScaler(), RidgeCV(alphas=np.logspace(-2, 4, 25))) +gbm = lambda: HistGradientBoostingRegressor(max_iter=300, learning_rate=0.05, max_leaf_nodes=15, min_samples_leaf=40, + l2_regularization=1.0, random_state=0) +kf = KFold(10, shuffle=True, random_state=0) +gk = GroupKFold(n_splits=len(set(huc))) +for tname, target in [("LS k*", ktr), ("volume ratio", vtr)]: + for mname, fn in [("ridge", ridge), ("GBM", gbm)]: + for sname, sp, grp in [("10-fold by gauge", kf, None), ("leave-one-HUC2-out", gk, huc)]: + mult, r2 = oof(target, fn, sp, grp) + say(f" target {tname}, {mname}, {sname}: out-of-fold R2 of log target {r2:.3f}") + score(mult, f"attr {mname} on log {tname}, {sname}") + if tname == "LS k*" and mname == "GBM" and sname == "10-fold by gauge": + res["_mult_attr_gbm"] = mult.tolist() + +say("\n-- per-gauge scalars (gauged calibration; the ceiling of any regionalized multiplier)") +m_g = np.where(fit_ok, ktr, 1.0) +score(m_g, "per-gauge LS k* fitted on training years") +m_gv = np.where(fit_ok, vtr, 1.0) +score(m_gv, "per-gauge volume ratio fitted on training years") +# shrink the per-gauge scalar halfway to 1 (a common hedge against training-year noise) +score(np.sqrt(m_g), "per-gauge LS k*, shrunk halfway in log space") +score(np.where(np.isfinite(kte), kte, 1.0), "per-gauge LS k* fitted ON TEST YEARS (in-sample oracle)") + +# which gauges move the median, for the best honest regional scheme +T = pd.DataFrame(dict(nse_test=base_n, kge_test=base_k, nse_train=Mtr.nse.values, k_train=ktr, v_train=vtr, k_test=kte, + v_test=vte), index=ids) +T.to_csv(C.HERE / f"volume_by_gauge_s{seed}.csv") +open(C.HERE / f"volume_s{seed}.txt", "w").write("\n".join(out) + "\n") +json.dump({k: v for k, v in res.items() if not k.startswith("_")}, open(C.HERE / f"volume_s{seed}.json", "w"), indent=1, + default=float) +np.save(C.HERE / f"mult_attr_gbm_s{seed}.npy", np.array(res.get("_mult_attr_gbm", []))) +print("\n".join(out)) diff --git a/experiments/levers/volume_by_gauge_s42.csv b/experiments/levers/volume_by_gauge_s42.csv new file mode 100644 index 00000000..74e9202b --- /dev/null +++ b/experiments/levers/volume_by_gauge_s42.csv @@ -0,0 +1,2366 @@ +,nse_test,kge_test,nse_train,k_train,v_train,k_test,v_test +14190500,0.8943331688560217,0.9206877447476361,0.9441374619084377,1.0175226360524137,1.0365352054460484,0.9356552102085612,0.9453358480844452 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+01017000,0.9116571144632746,0.9338364057895292,0.8940787464331243,0.9997857394630129,1.0256284774215,0.9607650742429532,0.9536101349327987 diff --git a/experiments/levers/volume_by_year.py b/experiments/levers/volume_by_year.py new file mode 100644 index 00000000..f09adecd --- /dev/null +++ b/experiments/levers/volume_by_year.py @@ -0,0 +1,29 @@ +"""Median over gauges of predicted/observed volume by water year, WY1982-2010 (replay for WY1982-1995, source run for +WY1996-2010), plus observed and predicted median flow index. Usage: volume_by_year.py . +Writes volume_by_year_.txt.""" +import sys + +import numpy as np + +import common as C + +src, rep = sys.argv[1], sys.argv[2] +ids, tt, PT, OT = C.load_preds(src) +rid, tr, PR, OR = C.load_preds(rep) +PR, OR = C.align(rid, PR, ids), C.align(rid, OR, ids) +out = ["WY median pred/obs vol share>1 median obs mean (m3/s, per-gauge relative to its 1982-2010 mean)"] +wyr, wyt = C.water_year(tr), C.water_year(tt) +allobs = [] +per = {} +for y in range(1982, 2011): + P, O = (PR[:, wyr == y], OR[:, wyr == y]) if y <= 1995 else (PT[:, wyt == y], OT[:, wyt == y]) + m = np.isfinite(P) & np.isfinite(O) + ok = m.sum(1) >= 300 + r = np.where(ok, np.where(m, P, 0).sum(1) / np.where(m, O, 0).sum(1), np.nan) + om = np.where(ok, np.where(m, O, 0).sum(1) / np.maximum(m.sum(1), 1), np.nan) + per[y] = (r, om) +clim = np.nanmean(np.stack([v[1] for v in per.values()]), axis=0) +for y, (r, om) in per.items(): + out.append(f"{y} {np.nanmedian(r):.4f} {np.nanmean(r > 1):.3f} {np.nanmedian(om / clim):.3f}") +open(C.HERE / f"volume_by_year_{src[:20]}.txt", "w").write("\n".join(out) + "\n") +print("\n".join(out)) diff --git a/experiments/levers/volume_by_year_2026-09-27T07-29-47Z.txt b/experiments/levers/volume_by_year_2026-09-27T07-29-47Z.txt new file mode 100644 index 00000000..862cc807 --- /dev/null +++ b/experiments/levers/volume_by_year_2026-09-27T07-29-47Z.txt @@ -0,0 +1,30 @@ +WY median pred/obs vol share>1 median obs mean (m3/s, per-gauge relative to its 1982-2010 mean) +1982 0.9791 0.400 1.021 +1983 0.9755 0.375 1.204 +1984 0.9699 0.361 1.226 +1985 0.9916 0.446 0.808 +1986 0.9807 0.409 1.034 +1987 0.9969 0.469 0.891 +1988 1.0204 0.529 0.655 +1989 1.0507 0.628 0.871 +1990 1.0269 0.571 0.939 +1991 1.0231 0.562 0.983 +1992 1.0563 0.638 0.767 +1993 1.0173 0.552 1.183 +1994 1.0208 0.559 0.920 +1995 1.0232 0.564 0.994 +1996 0.9993 0.497 1.243 +1997 1.0036 0.511 1.189 +1998 1.0263 0.568 1.149 +1999 1.0133 0.531 0.856 +2000 1.0763 0.649 0.737 +2001 1.0942 0.664 0.714 +2002 1.1064 0.684 0.686 +2003 1.0912 0.689 0.932 +2004 1.0607 0.625 0.939 +2005 1.0312 0.578 1.020 +2006 1.0629 0.629 0.865 +2007 1.0506 0.623 0.923 +2008 1.0757 0.658 0.998 +2009 1.0833 0.661 0.909 +2010 1.0547 0.633 1.024 diff --git a/experiments/levers/volume_by_year_2026-09-27T10-31-30Z.txt b/experiments/levers/volume_by_year_2026-09-27T10-31-30Z.txt new file mode 100644 index 00000000..230bf3cd --- /dev/null +++ b/experiments/levers/volume_by_year_2026-09-27T10-31-30Z.txt @@ -0,0 +1,30 @@ +WY median pred/obs vol share>1 median obs mean (m3/s, per-gauge relative to its 1982-2010 mean) +1982 0.9766 0.394 1.021 +1983 0.9719 0.368 1.204 +1984 0.9665 0.355 1.226 +1985 0.9890 0.436 0.808 +1986 0.9783 0.398 1.034 +1987 0.9916 0.458 0.891 +1988 1.0171 0.522 0.655 +1989 1.0473 0.619 0.871 +1990 1.0241 0.561 0.939 +1991 1.0188 0.553 0.983 +1992 1.0524 0.627 0.767 +1993 1.0144 0.545 1.183 +1994 1.0171 0.548 0.920 +1995 1.0203 0.556 0.994 +1996 0.9951 0.485 1.243 +1997 1.0003 0.501 1.189 +1998 1.0215 0.560 1.149 +1999 1.0084 0.523 0.856 +2000 1.0730 0.641 0.737 +2001 1.0875 0.655 0.714 +2002 1.1034 0.679 0.686 +2003 1.0878 0.682 0.932 +2004 1.0568 0.615 0.939 +2005 1.0284 0.573 1.020 +2006 1.0591 0.624 0.865 +2007 1.0473 0.615 0.923 +2008 1.0726 0.652 0.998 +2009 1.0777 0.657 0.909 +2010 1.0511 0.626 1.024 diff --git a/experiments/levers/volume_s42.json b/experiments/levers/volume_s42.json new file mode 100644 index 00000000..669f5a73 --- /dev/null +++ b/experiments/levers/volume_s42.json @@ -0,0 +1,713 @@ +{ + "replay_check": { + "rel_median": 0.006325768335448084, + "rel_p99": 0.6926358402040723, + "rel_max": 88.73475511791185, + "nse_absdiff_max": 100.17431751006734 + }, + "global scalar 0.96 (max training median NSE)": { + "nse": { + "name": "global scalar 0.96 (max training median NSE)", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7417588072521619, + "pop_change": 0.0026468775966701497, + "ci_lo": 0.0008046265136342601, + 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1.143350399105003 + }, + "per-gauge LS k*, shrunk halfway in log space": { + "nse": { + "name": "per-gauge LS k*, shrunk halfway in log space", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7458929759913482, + "pop_change": 0.006781046335856411, + "ci_lo": 0.00415196646348735, + "ci_hi": 0.010491718557861985, + "paired_median": 0.001707586101017844, + "share_up": 0.642706131078224, + "share_down": 0.3547568710359408, + "n": 2365 + }, + "kge": { + "name": "per-gauge LS k*, shrunk halfway in log space", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7725607964305913, + "pop_change": 0.013487313860979566, + "ci_lo": 0.008408990559117962, + "ci_hi": 0.01762408802584879, + "paired_median": 0.0035552909597372784, + "share_up": 0.6097251585623679, + "share_down": 0.387737843551797, + "n": 2365 + }, + "mult_median": 0.9982467761074649, + "mult_p10": 0.931619501918451, + "mult_p90": 1.068222762914632 + }, + "per-gauge LS k* fitted ON TEST YEARS (in-sample oracle)": { + "nse": { + "name": "per-gauge LS k* fitted ON TEST YEARS (in-sample oracle)", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.767682532481288, + "pop_change": 0.028570602825796265, + "ci_lo": 0.024212591077747203, + "ci_hi": 0.03349523209107636, + "paired_median": 0.012377161526199165, + "share_up": 0.9995771670190274, + "share_down": 0.0, + "n": 2365 + }, + "kge": { + "name": "per-gauge LS k* fitted ON TEST YEARS (in-sample oracle)", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7996604422096395, + "pop_change": 0.04058695964002779, + "ci_lo": 0.034901888146726186, + "ci_hi": 0.04658764537684636, + "paired_median": 0.011172953622586856, + "share_up": 0.6228329809725158, + "share_down": 0.3771670190274841, + "n": 2365 + }, + "mult_median": 0.9586990690157485, + "mult_p10": 0.7754023729643265, + "mult_p90": 1.186447028548956 + } +} \ No newline at end of file diff --git a/experiments/levers/volume_s42.txt b/experiments/levers/volume_s42.txt new file mode 100644 index 00000000..6ff3a5e5 --- /dev/null +++ b/experiments/levers/volume_s42.txt @@ -0,0 +1,63 @@ +== seed 42: source 2026-09-27T07-29-47Z-train-and-test, replay 2026-09-30T13-13-59Z-train-and-test +replay check, WY1996 from 1995-11-01 (334 days): per-gauge max relative difference median 6.33e-03, p99 6.93e-01, max 8.87e+01; |NSE diff| max 1.00e+02; obs identical True +training window 1981-11-01..1995-09-30 (5082 d); test 1995-10-02..2010-09-29 (5477 d) +test-year median NSE 0.7391 KGE 0.7591 (n=2365); training-year median NSE 0.7847 KGE 0.8087 (n=2365 with >= 365 valid days) +predicted/observed volume (1/v): training median 1.0044 (share > 1: 0.518); test median 1.0455 (share > 1: 0.649) +per-gauge LS scalar k*: training median 0.9964, test median 0.9587; Spearman(train k*, test k*) 0.552; Spearman(train vol ratio, test vol ratio) 0.654 + +-- global scalars (fit on training years) + scalar maximizing training median NSE: 0.96 (training median 0.7855 vs 0.7847) + global scalar 0.96 (max training median NSE) NSE median 0.7391 -> 0.7418 pop +0.0026 [+0.0008,+0.0066] paired +0.0023 up 57.2% down 42.8% + global scalar 0.96 (max training median NSE) KGE median 0.7591 -> 0.7584 pop -0.0006 [-0.0060,+0.0032] paired -0.0106 up 41.1% down 58.9% + global scalar 0.9964 (median per-gauge LS k*) NSE median 0.7391 -> 0.7396 pop +0.0005 [-0.0006,+0.0016] paired +0.0004 up 62.9% down 37.1% + global scalar 0.9964 (median per-gauge LS k*) KGE median 0.7591 -> 0.7595 pop +0.0004 [-0.0016,+0.0022] paired -0.0003 up 46.8% down 53.2% + global scalar 0.9956 (median volume ratio) NSE median 0.7391 -> 0.7397 pop +0.0006 [-0.0005,+0.0017] paired +0.0005 up 62.8% down 37.2% + global scalar 0.9956 (median volume ratio) KGE median 0.7591 -> 0.7590 pop -0.0001 [-0.0017,+0.0022] paired -0.0004 up 46.8% down 53.2% + fixed 0.95 (reference; chosen on test years earlier) NSE median 0.7391 -> 0.7438 pop +0.0047 [+0.0019,+0.0084] paired +0.0021 up 55.5% down 44.5% + fixed 0.95 (reference; chosen on test years earlier) KGE median 0.7591 -> 0.7542 pop -0.0049 [-0.0097,+0.0001] paired -0.0154 up 39.1% down 60.9% + +-- per-HUC2 scalars (fit on training years within each HUC2) + 01:0.995(n104) 02:1.005(n294) 03:0.989(n309) 04:0.985(n152) 05:1.006(n240) 06:0.993(n31) 07:0.988(n191) 08:1.046(n24) 09:1.012(n21) 10L:1.013(n101) 10U:1.015(n76) 11:1.003(n74) 12:1.037(n57) 13:0.982(n39) 14:0.927(n103) 15:1.018(n54) 16:0.975(n60) 17:0.989(n254) 18:1.029(n181) + per-HUC2 scalar (max training median NSE in the HUC2) NSE median 0.7391 -> 0.7373 pop -0.0018 [-0.0036,+0.0005] paired -0.0007 up 42.7% down 57.3% + per-HUC2 scalar (max training median NSE in the HUC2) KGE median 0.7591 -> 0.7566 pop -0.0025 [-0.0060,+0.0010] paired -0.0021 up 43.9% down 56.1% + per-HUC2 scalar (median per-gauge LS k* in the HUC2) NSE median 0.7391 -> 0.7397 pop +0.0006 [-0.0008,+0.0030] paired +0.0001 up 51.8% down 48.2% + per-HUC2 scalar (median per-gauge LS k* in the HUC2) KGE median 0.7591 -> 0.7593 pop +0.0002 [-0.0024,+0.0032] paired +0.0004 up 51.3% down 48.7% + per-HUC2 scalar (median volume ratio in the HUC2) NSE median 0.7391 -> 0.7397 pop +0.0006 [-0.0008,+0.0025] paired +0.0001 up 52.9% down 47.1% + per-HUC2 scalar (median volume ratio in the HUC2) KGE median 0.7591 -> 0.7604 pop +0.0013 [-0.0011,+0.0040] paired +0.0005 up 52.9% down 47.1% + +-- attribute-conditioned multiplier: predict log training k* from catchment attributes, out-of-fold by gauge + gauges usable for fitting: 2359 (training NSE finite, 0.2 < k* < 5); features: 17 + target LS k*, ridge, 10-fold by gauge: out-of-fold R2 of log target 0.015 + attr ridge on log LS k*, 10-fold by gauge NSE median 0.7391 -> 0.7396 pop +0.0005 [-0.0016,+0.0028] paired +0.0002 up 54.5% down 45.5% + attr ridge on log LS k*, 10-fold by gauge KGE median 0.7591 -> 0.7586 pop -0.0005 [-0.0035,+0.0022] paired -0.0000 up 49.3% down 50.7% + target LS k*, ridge, leave-one-HUC2-out: out-of-fold R2 of log target -0.006 + attr ridge on log LS k*, leave-one-HUC2-out NSE median 0.7391 -> 0.7376 pop -0.0015 [-0.0032,+0.0010] paired +0.0001 up 51.5% down 48.5% + attr ridge on log LS k*, leave-one-HUC2-out KGE median 0.7591 -> 0.7583 pop -0.0008 [-0.0043,+0.0019] paired -0.0005 up 47.2% down 52.8% + target LS k*, GBM, 10-fold by gauge: out-of-fold R2 of log target -0.047 + attr GBM on log LS k*, 10-fold by gauge NSE median 0.7391 -> 0.7387 pop -0.0004 [-0.0033,+0.0031] paired +0.0000 up 50.8% down 49.2% + attr GBM on log LS k*, 10-fold by gauge KGE median 0.7591 -> 0.7566 pop -0.0025 [-0.0074,+0.0022] paired -0.0012 up 46.0% down 54.0% + target LS k*, GBM, leave-one-HUC2-out: out-of-fold R2 of log target -0.119 + attr GBM on log LS k*, leave-one-HUC2-out NSE median 0.7391 -> 0.7376 pop -0.0015 [-0.0053,+0.0016] paired -0.0003 up 47.0% down 53.0% + attr GBM on log LS k*, leave-one-HUC2-out KGE median 0.7591 -> 0.7501 pop -0.0090 [-0.0141,-0.0035] paired -0.0025 up 45.0% down 55.0% + target volume ratio, ridge, 10-fold by gauge: out-of-fold R2 of log target -0.007 + attr ridge on log volume ratio, 10-fold by gauge NSE median 0.7391 -> 0.7398 pop +0.0007 [-0.0004,+0.0028] paired +0.0002 up 57.3% down 42.7% + attr ridge on log volume ratio, 10-fold by gauge KGE median 0.7591 -> 0.7594 pop +0.0004 [-0.0022,+0.0023] paired -0.0004 up 45.8% down 54.2% + target volume ratio, ridge, leave-one-HUC2-out: out-of-fold R2 of log target -0.031 + attr ridge on log volume ratio, leave-one-HUC2-out NSE median 0.7391 -> 0.7391 pop -0.0001 [-0.0020,+0.0016] paired +0.0002 up 54.9% down 45.1% + attr ridge on log volume ratio, leave-one-HUC2-out KGE median 0.7591 -> 0.7591 pop +0.0000 [-0.0025,+0.0024] paired -0.0004 up 46.8% down 53.2% + target volume ratio, GBM, 10-fold by gauge: out-of-fold R2 of log target -0.065 + attr GBM on log volume ratio, 10-fold by gauge NSE median 0.7391 -> 0.7375 pop -0.0016 [-0.0046,+0.0017] paired -0.0003 up 46.6% down 53.4% + attr GBM on log volume ratio, 10-fold by gauge KGE median 0.7591 -> 0.7542 pop -0.0049 [-0.0093,-0.0006] paired -0.0011 up 46.7% down 53.3% + target volume ratio, GBM, leave-one-HUC2-out: out-of-fold R2 of log target -0.145 + attr GBM on log volume ratio, leave-one-HUC2-out NSE median 0.7391 -> 0.7355 pop -0.0036 [-0.0070,-0.0008] paired -0.0010 up 42.0% down 58.0% + attr GBM on log volume ratio, leave-one-HUC2-out KGE median 0.7591 -> 0.7463 pop -0.0128 [-0.0180,-0.0079] paired -0.0026 up 43.0% down 57.0% + +-- per-gauge scalars (gauged calibration; the ceiling of any regionalized multiplier) + per-gauge LS k* fitted on training years NSE median 0.7391 -> 0.7469 pop +0.0078 [+0.0042,+0.0123] paired +0.0018 up 58.9% down 40.8% + per-gauge LS k* fitted on training years KGE median 0.7591 -> 0.7706 pop +0.0116 [+0.0064,+0.0178] paired +0.0022 up 54.8% down 44.9% + per-gauge volume ratio fitted on training years NSE median 0.7391 -> 0.7439 pop +0.0047 [+0.0011,+0.0089] paired +0.0000 up 50.2% down 49.5% + per-gauge volume ratio fitted on training years KGE median 0.7591 -> 0.7713 pop +0.0122 [+0.0056,+0.0173] paired +0.0024 up 54.4% down 45.3% + per-gauge LS k*, shrunk halfway in log space NSE median 0.7391 -> 0.7459 pop +0.0068 [+0.0042,+0.0105] paired +0.0017 up 64.3% down 35.5% + per-gauge LS k*, shrunk halfway in log space KGE median 0.7591 -> 0.7726 pop +0.0135 [+0.0084,+0.0176] paired +0.0036 up 61.0% down 38.8% + per-gauge LS k* fitted ON TEST YEARS (in-sample oracle) NSE median 0.7391 -> 0.7677 pop +0.0286 [+0.0242,+0.0335] paired +0.0124 up 100.0% down 0.0% + per-gauge LS k* fitted ON TEST YEARS (in-sample oracle) KGE median 0.7591 -> 0.7997 pop +0.0406 [+0.0349,+0.0466] paired +0.0112 up 62.3% down 37.7% diff --git a/experiments/levers/volume_s43.json b/experiments/levers/volume_s43.json new file mode 100644 index 00000000..a9b6e7de --- /dev/null +++ b/experiments/levers/volume_s43.json @@ -0,0 +1,713 @@ +{ + "replay_check": { + "rel_median": 0.006943255562261034, + "rel_p99": 0.6795701618465986, + "rel_max": 71.18772885587532, + "nse_absdiff_max": 77.031767078146 + }, + "global scalar 1.0025 (max training median NSE)": { + "nse": { + "name": "global scalar 1.0025 (max training median NSE)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7335949770373908, + "pop_change": -0.0005122612842486385, + "ci_lo": -0.0011967715709417792, + "ci_hi": 0.00043836231629129055, + "paired_median": -0.00027227469752832967, + "share_up": 0.3704016913319239, + "share_down": 0.6295983086680761, + "n": 2365 + }, + "kge": { + "name": "global scalar 1.0025 (max training median NSE)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7585904090479152, + "pop_change": -0.0002044909557918917, + "ci_lo": -0.0016424766985472239, + "ci_hi": 0.001380789535770699, + "paired_median": 0.00021283313229991663, + "share_up": 0.533615221987315, + "share_down": 0.46638477801268496, + "n": 2365 + }, + "mult_median": 1.0025, + "mult_p10": 1.0025, + "mult_p90": 1.0025 + }, + "global scalar 0.9982 (median per-gauge LS k*)": { + "nse": { + "name": "global scalar 0.9982 (median per-gauge LS k*)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7345811473121389, + "pop_change": 0.0004739089904993721, + "ci_lo": -0.00037816044162031215, + "ci_hi": 0.0010443298490886278, + "paired_median": 0.0001842058891234899, + "share_up": 0.6228329809725158, + "share_down": 0.3771670190274841, + "n": 2365 + }, + "kge": { + "name": "global scalar 0.9982 (median per-gauge LS k*)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7594027614377876, + "pop_change": 0.0006078614340805366, + "ci_lo": -0.0011126848524963817, + "ci_hi": 0.0015273420361759937, + "paired_median": -0.00019661249938840442, + "share_up": 0.45792811839323466, + "share_down": 0.5420718816067653, + "n": 2365 + }, + "mult_median": 0.9982203412717038, + "mult_p10": 0.9982203412717038, + "mult_p90": 0.9982203412717038 + }, + "global scalar 0.9989 (median volume ratio)": { + "nse": { + "name": "global scalar 0.9989 (median volume ratio)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7342286545259319, + "pop_change": 0.00012141620429240163, + "ci_lo": -0.00029110804571907956, + "ci_hi": 0.0007089606715883481, + "paired_median": 0.00011651922937350179, + "share_up": 0.6245243128964059, + "share_down": 0.3754756871035941, + "n": 2365 + }, + "kge": { + "name": "global scalar 0.9989 (median volume ratio)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.759475024508336, + "pop_change": 0.0006801245046289539, + "ci_lo": -0.0009270449958271504, + "ci_hi": 0.0010895760441764768, + "paired_median": -0.00012121049982749543, + "share_up": 0.45961945031712476, + "share_down": 0.5403805496828753, + "n": 2365 + }, + "mult_median": 0.9988856215882379, + "mult_p10": 0.9988856215882379, + "mult_p90": 0.9988856215882379 + }, + "fixed 0.95 (reference; chosen on test years earlier)": { + "nse": { + "name": "fixed 0.95 (reference; chosen on test years earlier)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.737024021998081, + "pop_change": 0.002916783676441548, + "ci_lo": 0.0004201789057842742, + "ci_hi": 0.007453334568319125, + "paired_median": 0.001966860315145036, + "share_up": 0.5463002114164904, + "share_down": 0.4536997885835095, + "n": 2365 + }, + "kge": { + "name": "fixed 0.95 (reference; chosen on test years earlier)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.751709473980767, + "pop_change": -0.0070854260229400445, + "ci_lo": -0.012824714107983342, + "ci_hi": -0.0036466066858152815, + "paired_median": -0.016836319296809732, + "share_up": 0.3758985200845666, + "share_down": 0.6241014799154334, + "n": 2365 + }, + "mult_median": 0.95, + "mult_p10": 0.95, + "mult_p90": 0.95 + }, + "huc2_scalars": { + "01": { + "n": 104, + "k_med": 0.99, + "k_ls": 0.9949955658699408, + "k_v": 1.0158936355818016 + }, + "02": { + "n": 294, + "k_med": 1.03, + "k_ls": 1.0066932467030871, + "k_v": 1.009232894096105 + }, + "03": { + "n": 309, + "k_med": 0.9825, + "k_ls": 0.9933368574712973, + "k_v": 0.9870344520471348 + }, + "04": { + "n": 152, + "k_med": 0.985, + "k_ls": 0.9844187622397835, + "k_v": 1.0003549561782965 + }, + "05": { + "n": 240, + "k_med": 1.0375, + "k_ls": 1.0080751597993935, + "k_v": 1.011680538816929 + }, + "06": { + "n": 31, + "k_med": 1.0825, + "k_ls": 0.9974372929123604, + "k_v": 1.0058322030423332 + }, + "07": { + "n": 191, + "k_med": 0.9775, + "k_ls": 0.9807168035428439, + "k_v": 0.9904652670682033 + }, + "08": { + "n": 24, + "k_med": 1.055, + "k_ls": 1.045834707686344, + "k_v": 1.0618180654758937 + }, + "09": { + "n": 21, + "k_med": 0.8925, + "k_ls": 1.015776080044117, + "k_v": 1.0230609354840388 + }, + "10L": { + "n": 101, + "k_med": 0.9825, + "k_ls": 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training median NSE in the HUC2)": { + "nse": { + "name": "per-HUC2 scalar (max training median NSE in the HUC2)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7343476703401838, + "pop_change": 0.00024043201854428986, + "ci_lo": -0.0023725021921530975, + "ci_hi": 0.0025044199519785836, + "paired_median": 3.1844142431181055e-05, + "share_up": 0.5044397463002114, + "share_down": 0.49556025369978857, + "n": 2365 + }, + "kge": { + "name": "per-HUC2 scalar (max training median NSE in the HUC2)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7588913494890004, + "pop_change": 9.6449485293304e-05, + "ci_lo": -0.0047190136538947545, + "ci_hi": 0.0032280087934378455, + "paired_median": -0.0013631254026766904, + "share_up": 0.47610993657505285, + "share_down": 0.5238900634249471, + "n": 2365 + }, + "mult_median": 0.99, + "mult_p10": 0.9775, + "mult_p90": 1.0375 + }, + "per-HUC2 scalar (median per-gauge LS k* in the HUC2)": { + "nse": { + 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ON TEST YEARS (in-sample oracle)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7979413732641474, + "pop_change": 0.039146473260440295, + "ci_lo": 0.03113358781707016, + "ci_hi": 0.043470685626859365, + "paired_median": 0.00986913452724103, + "share_up": 0.6173361522198731, + "share_down": 0.38266384778012685, + "n": 2365 + }, + "mult_median": 0.9604134360835244, + "mult_p10": 0.7807372255033203, + "mult_p90": 1.1949161075715375 + } +} \ No newline at end of file diff --git a/experiments/levers/volume_s43.txt b/experiments/levers/volume_s43.txt new file mode 100644 index 00000000..fd13e97e --- /dev/null +++ b/experiments/levers/volume_s43.txt @@ -0,0 +1,63 @@ +== seed 43: source 2026-09-27T10-31-30Z-train-and-test, replay 2026-09-30T13-50-59Z-train-and-test +replay check, WY1996 from 1995-11-01 (334 days): per-gauge max relative difference median 6.94e-03, p99 6.80e-01, max 7.12e+01; |NSE diff| max 7.70e+01; obs identical True +training window 1981-11-01..1995-09-30 (5082 d); test 1995-10-02..2010-09-29 (5477 d) +test-year median NSE 0.7341 KGE 0.7588 (n=2365); training-year median NSE 0.7822 KGE 0.8035 (n=2365 with >= 365 valid days) +predicted/observed volume (1/v): training median 1.0011 (share > 1: 0.505); test median 1.0417 (share > 1: 0.639) +per-gauge LS scalar k*: training median 0.9982, test median 0.9604; Spearman(train k*, test k*) 0.554; Spearman(train vol ratio, test vol ratio) 0.654 + +-- global scalars (fit on training years) + scalar maximizing training median NSE: 1.0025 (training median 0.7825 vs 0.7822) + global scalar 1.0025 (max training median NSE) NSE median 0.7341 -> 0.7336 pop -0.0005 [-0.0012,+0.0004] paired -0.0003 up 37.0% down 63.0% + global scalar 1.0025 (max training median NSE) KGE median 0.7588 -> 0.7586 pop -0.0002 [-0.0016,+0.0014] paired +0.0002 up 53.4% down 46.6% + global scalar 0.9982 (median per-gauge LS k*) NSE median 0.7341 -> 0.7346 pop +0.0005 [-0.0004,+0.0010] paired +0.0002 up 62.3% down 37.7% + global scalar 0.9982 (median per-gauge LS k*) KGE median 0.7588 -> 0.7594 pop +0.0006 [-0.0011,+0.0015] paired -0.0002 up 45.8% down 54.2% + global scalar 0.9989 (median volume ratio) NSE median 0.7341 -> 0.7342 pop +0.0001 [-0.0003,+0.0007] paired +0.0001 up 62.5% down 37.5% + global scalar 0.9989 (median volume ratio) KGE median 0.7588 -> 0.7595 pop +0.0007 [-0.0009,+0.0011] paired -0.0001 up 46.0% down 54.0% + fixed 0.95 (reference; chosen on test years earlier) NSE median 0.7341 -> 0.7370 pop +0.0029 [+0.0004,+0.0075] paired +0.0020 up 54.6% down 45.4% + fixed 0.95 (reference; chosen on test years earlier) KGE median 0.7588 -> 0.7517 pop -0.0071 [-0.0128,-0.0036] paired -0.0168 up 37.6% down 62.4% + +-- per-HUC2 scalars (fit on training years within each HUC2) + 01:0.995(n104) 02:1.007(n294) 03:0.993(n309) 04:0.984(n152) 05:1.008(n240) 06:0.997(n31) 07:0.981(n191) 08:1.046(n24) 09:1.016(n21) 10L:1.008(n101) 10U:1.016(n76) 11:1.011(n74) 12:1.060(n57) 13:0.986(n39) 14:0.927(n103) 15:1.049(n54) 16:0.976(n60) 17:0.990(n254) 18:1.030(n181) + per-HUC2 scalar (max training median NSE in the HUC2) NSE median 0.7341 -> 0.7343 pop +0.0002 [-0.0024,+0.0025] paired +0.0000 up 50.4% down 49.6% + per-HUC2 scalar (max training median NSE in the HUC2) KGE median 0.7588 -> 0.7589 pop +0.0001 [-0.0047,+0.0032] paired -0.0014 up 47.6% down 52.4% + per-HUC2 scalar (median per-gauge LS k* in the HUC2) NSE median 0.7341 -> 0.7349 pop +0.0008 [-0.0015,+0.0021] paired +0.0001 up 52.4% down 47.6% + per-HUC2 scalar (median per-gauge LS k* in the HUC2) KGE median 0.7588 -> 0.7593 pop +0.0005 [-0.0036,+0.0024] paired +0.0005 up 51.7% down 48.3% + per-HUC2 scalar (median volume ratio in the HUC2) NSE median 0.7341 -> 0.7342 pop +0.0001 [-0.0014,+0.0019] paired -0.0000 up 49.9% down 50.1% + per-HUC2 scalar (median volume ratio in the HUC2) KGE median 0.7588 -> 0.7600 pop +0.0012 [-0.0020,+0.0031] paired +0.0002 up 51.7% down 48.3% + +-- attribute-conditioned multiplier: predict log training k* from catchment attributes, out-of-fold by gauge + gauges usable for fitting: 2359 (training NSE finite, 0.2 < k* < 5); features: 17 + target LS k*, ridge, 10-fold by gauge: out-of-fold R2 of log target 0.016 + attr ridge on log LS k*, 10-fold by gauge NSE median 0.7341 -> 0.7338 pop -0.0003 [-0.0020,+0.0015] paired +0.0000 up 50.7% down 49.3% + attr ridge on log LS k*, 10-fold by gauge KGE median 0.7588 -> 0.7577 pop -0.0010 [-0.0053,+0.0007] paired -0.0003 up 48.3% down 51.7% + target LS k*, ridge, leave-one-HUC2-out: out-of-fold R2 of log target -0.006 + attr ridge on log LS k*, leave-one-HUC2-out NSE median 0.7341 -> 0.7326 pop -0.0015 [-0.0035,+0.0004] paired -0.0001 up 46.9% down 53.1% + attr ridge on log LS k*, leave-one-HUC2-out KGE median 0.7588 -> 0.7563 pop -0.0025 [-0.0067,-0.0005] paired -0.0009 up 45.2% down 54.8% + target LS k*, GBM, 10-fold by gauge: out-of-fold R2 of log target -0.037 + attr GBM on log LS k*, 10-fold by gauge NSE median 0.7341 -> 0.7333 pop -0.0008 [-0.0040,+0.0021] paired +0.0001 up 51.2% down 48.8% + attr GBM on log LS k*, 10-fold by gauge KGE median 0.7588 -> 0.7549 pop -0.0039 [-0.0088,-0.0004] paired -0.0009 up 47.4% down 52.6% + target LS k*, GBM, leave-one-HUC2-out: out-of-fold R2 of log target -0.112 + attr GBM on log LS k*, leave-one-HUC2-out NSE median 0.7341 -> 0.7302 pop -0.0039 [-0.0067,-0.0005] paired -0.0004 up 46.1% down 53.9% + attr GBM on log LS k*, leave-one-HUC2-out KGE median 0.7588 -> 0.7500 pop -0.0088 [-0.0148,-0.0047] paired -0.0033 up 43.7% down 56.3% + target volume ratio, ridge, 10-fold by gauge: out-of-fold R2 of log target -0.003 + attr ridge on log volume ratio, 10-fold by gauge NSE median 0.7341 -> 0.7340 pop -0.0001 [-0.0015,+0.0018] paired -0.0000 up 49.8% down 50.2% + attr ridge on log volume ratio, 10-fold by gauge KGE median 0.7588 -> 0.7579 pop -0.0009 [-0.0048,+0.0008] paired -0.0004 up 45.4% down 54.6% + target volume ratio, ridge, leave-one-HUC2-out: out-of-fold R2 of log target -0.031 + attr ridge on log volume ratio, leave-one-HUC2-out NSE median 0.7341 -> 0.7335 pop -0.0006 [-0.0026,+0.0009] paired -0.0001 up 46.2% down 53.8% + attr ridge on log volume ratio, leave-one-HUC2-out KGE median 0.7588 -> 0.7567 pop -0.0021 [-0.0055,+0.0001] paired -0.0010 up 43.0% down 57.0% + target volume ratio, GBM, 10-fold by gauge: out-of-fold R2 of log target -0.063 + attr GBM on log volume ratio, 10-fold by gauge NSE median 0.7341 -> 0.7331 pop -0.0011 [-0.0047,+0.0013] paired -0.0004 up 44.8% down 55.2% + attr GBM on log volume ratio, 10-fold by gauge KGE median 0.7588 -> 0.7536 pop -0.0052 [-0.0106,-0.0015] paired -0.0012 up 46.0% down 54.0% + target volume ratio, GBM, leave-one-HUC2-out: out-of-fold R2 of log target -0.120 + attr GBM on log volume ratio, leave-one-HUC2-out NSE median 0.7341 -> 0.7285 pop -0.0056 [-0.0081,-0.0019] paired -0.0011 up 41.0% down 59.0% + attr GBM on log volume ratio, leave-one-HUC2-out KGE median 0.7588 -> 0.7448 pop -0.0140 [-0.0198,-0.0092] paired -0.0030 up 42.7% down 57.3% + +-- per-gauge scalars (gauged calibration; the ceiling of any regionalized multiplier) + per-gauge LS k* fitted on training years NSE median 0.7341 -> 0.7435 pop +0.0094 [+0.0047,+0.0129] paired +0.0018 up 58.7% down 41.0% + per-gauge LS k* fitted on training years KGE median 0.7588 -> 0.7707 pop +0.0119 [+0.0055,+0.0169] paired +0.0022 up 54.0% down 45.7% + per-gauge volume ratio fitted on training years NSE median 0.7341 -> 0.7382 pop +0.0041 [-0.0000,+0.0080] paired -0.0000 up 49.5% down 50.3% + per-gauge volume ratio fitted on training years KGE median 0.7588 -> 0.7694 pop +0.0106 [+0.0043,+0.0151] paired +0.0024 up 54.1% down 45.7% + per-gauge LS k*, shrunk halfway in log space NSE median 0.7341 -> 0.7412 pop +0.0071 [+0.0042,+0.0102] paired +0.0018 up 63.9% down 35.8% + per-gauge LS k*, shrunk halfway in log space KGE median 0.7588 -> 0.7707 pop +0.0119 [+0.0066,+0.0150] paired +0.0035 up 61.0% down 38.7% + per-gauge LS k* fitted ON TEST YEARS (in-sample oracle) NSE median 0.7341 -> 0.7630 pop +0.0289 [+0.0250,+0.0331] paired +0.0125 up 100.0% down 0.0% + per-gauge LS k* fitted ON TEST YEARS (in-sample oracle) KGE median 0.7588 -> 0.7979 pop +0.0391 [+0.0311,+0.0435] paired +0.0099 up 61.7% down 38.3% diff --git a/experiments/levers/volume_state.py b/experiments/levers/volume_state.py new file mode 100644 index 00000000..f8a38bdd --- /dev/null +++ b/experiments/levers/volume_state.py @@ -0,0 +1,125 @@ +"""Lever 1b: STATE-dependent runoff corrections, fitted on TRAINING years (WY1982-1995), scored on TEST years. + +The constant multiplier fails because the Q' bias is dry-year over-prediction plus a post-2000 drift (volume_by_year). +These corrections use only the model's own predictions at test time (no observations), with parameters fitted on the +training years: + power : p' = a * pbar * (p / pbar)^b pbar = the gauge's training-year mean prediction (global a, b; grid) + state : p' = p * (A(t) / pbar)^c A(t) = trailing 365-day mean prediction (global c; grid) + qmap : per-gauge empirical quantile mapping, training-year predicted -> observed quantiles (gauged ceiling) + qmap_g : global quantile mapping on p / pbar pooled over gauges +Usage: volume_state.py . Writes volume_state_s.txt / .json. +""" +import json +import sys + +import numpy as np +import pandas as pd + +import common as C + +seed, replay = int(sys.argv[1]), sys.argv[2] +src = {42: C.S42, 43: C.S43}[seed] +out, res = [], {} +say = out.append +ids, tt, PT, OT = C.load_preds(src) +rid, tr, PR, OR = C.load_preds(replay) +PR, OR = C.align(rid, PR, ids), C.align(rid, OR, ids) +wyr, wyt = C.water_year(tr), C.water_year(tt) +trn = (wyr >= 1982) & (wyr <= 1995) & (tr >= pd.Timestamp("1981-11-01")) +tst = (wyt >= 1996) & (wyt <= 2010) +Ptr, Otr = PR[:, trn], OR[:, trn] +Pte, Ote = PT[:, tst], OT[:, tst] +pbar = np.nanmean(Ptr, axis=1) +B = C.metrics(Pte, Ote) +bn, bk = B.nse.values, B.kge.values +say(f"== seed {seed}: state-dependent corrections, fit WY1982-1995, score WY1996-2010 (base {np.nanmedian(bn):.4f} / " + f"{np.nanmedian(bk):.4f})") + + +def score(P, name): + M = C.metrics(P, Ote) + s, sk = C.summarize(name, M.nse.values, bn), C.summarize(name, M.kge.values, bk, "KGE") + say(C.fmt(s)) + say(C.fmt(sk)) + res[name] = dict(nse=s, kge=sk) + + +# power law (global a, b), fitted to maximize the training median NSE +x_tr = Ptr / pbar[:, None] +x_te = Pte / pbar[:, None] +best = (-np.inf, 1.0, 1.0) +for b in np.round(np.arange(0.85, 1.1501, 0.025), 3): + xb = np.power(np.maximum(x_tr, 0), b) + for a in np.round(np.arange(0.90, 1.1001, 0.01), 3): + v = np.nanmedian(C.metrics(a * pbar[:, None] * xb, Otr).nse.values) + if v > best[0]: + best = (v, a, b) +say(f" power law fit: a {best[1]}, b {best[2]} (training median {best[0]:.4f} vs " + f"{np.nanmedian(C.metrics(Ptr, Otr).nse.values):.4f})") +score(best[1] * pbar[:, None] * np.power(np.maximum(x_te, 0), best[2]), f"power law a={best[1]} b={best[2]}") + + +# trailing-year state multiplier, computed on the full series (test days see the preceding year, which for WY1996 lies +# in the replay's WY1995: use the replay there so the state is continuous) +def trailing(P, n=365): + c = np.nancumsum(np.where(np.isfinite(P), P, 0), axis=1) + k = np.cumsum(np.isfinite(P), axis=1) + s = c.copy() + s[:, n:] = c[:, n:] - c[:, :-n] + kk = k.copy() + kk[:, n:] = k[:, n:] - k[:, :-n] + return s / np.maximum(kk, 1) + + +A_r = trailing(PR) +# test state: concatenate the replay's last 365 days before 1995-10-01 with the source run's test series +pre = (tr < pd.Timestamp("1995-10-01")) & (tr >= pd.Timestamp("1994-10-01")) +cat = np.concatenate([PR[:, pre], PT], axis=1) +A_t = trailing(cat)[:, pre.sum():][:, tst] +A_tr = A_r[:, trn] +best = (-np.inf, 0.0) +for c in np.round(np.arange(-0.6, 0.6001, 0.05), 3): + m = np.power(np.clip(A_tr / pbar[:, None], 0.05, 20), c) + v = np.nanmedian(C.metrics(Ptr * m, Otr).nse.values) + if v > best[0]: + best = (v, c) +say(f" trailing-year state exponent c = {best[1]} (training median {best[0]:.4f})") +score(Pte * np.power(np.clip(A_t / pbar[:, None], 0.05, 20), best[1]), f"trailing-year state multiplier c={best[1]}") +for c in [0.2, 0.4]: + score(Pte * np.power(np.clip(A_t / pbar[:, None], 0.05, 20), c), f" sensitivity: state multiplier c={c} (not fitted)") + +# quantile mapping +qs = np.linspace(0.005, 0.995, 199) + + +def qmap(p_fit, o_fit, p_apply): + ok = np.isfinite(p_fit) & np.isfinite(o_fit) + if ok.sum() < 365: + return p_apply + qp = np.quantile(p_fit[ok], qs) + qo = np.quantile(o_fit[ok], qs) + qp = np.maximum.accumulate(qp + np.arange(len(qs)) * 1e-12) + y = np.interp(p_apply, qp, qo) + # beyond the fitted range keep the edge ratio + hi = p_apply > qp[-1] + y[hi] = p_apply[hi] * qo[-1] / max(qp[-1], 1e-9) + lo = p_apply < qp[0] + y[lo] = p_apply[lo] * qo[0] / max(qp[0], 1e-9) + return y + + +Pq = np.stack([qmap(Ptr[g], Otr[g], Pte[g]) for g in range(len(ids))]) +score(Pq, "per-gauge quantile mapping (gauged ceiling)") +# global quantile mapping on normalized flow +xo_tr = Otr / pbar[:, None] +ok = np.isfinite(x_tr) & np.isfinite(xo_tr) +qp = np.quantile(x_tr[ok], qs) +qo = np.quantile(xo_tr[ok], qs) +say(f" global normalized quantiles (pred -> obs): q05 {qp[9]:.3f}->{qo[9]:.3f}, q50 {qp[99]:.3f}->{qo[99]:.3f}, " + f"q95 {qp[189]:.3f}->{qo[189]:.3f}") +Pg = pbar[:, None] * np.interp(x_te, qp, qo, right=np.nan) +Pg = np.where(np.isfinite(Pg), Pg, Pte * qo[-1] / qp[-1]) +score(Pg, "global quantile mapping on p / pbar") +open(C.HERE / f"volume_state_s{seed}.txt", "w").write("\n".join(out) + "\n") +json.dump(res, open(C.HERE / f"volume_state_s{seed}.json", "w"), indent=1, default=float) +print("\n".join(out)) diff --git a/experiments/levers/volume_state_s42.json b/experiments/levers/volume_state_s42.json new file mode 100644 index 00000000..7a023e29 --- /dev/null +++ b/experiments/levers/volume_state_s42.json @@ -0,0 +1,170 @@ +{ + "power law a=0.96 b=1.0": { + "nse": { + "name": "power law a=0.96 b=1.0", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7417588072521619, + "pop_change": 0.0026468775966701497, + "ci_lo": 0.0008046265136342629, + "ci_hi": 0.0066369264633769675, + "paired_median": 0.0023251397781485217, + "share_up": 0.5720930232558139, + "share_down": 0.42790697674418604, + "n": 2365 + }, + "kge": { + "name": "power law a=0.96 b=1.0", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7584449991238991, + "pop_change": -0.0006284834457126465, + "ci_lo": -0.006013478475187589, + "ci_hi": 0.0031536293990692154, + "paired_median": -0.010649857984882383, + "share_up": 0.41057082452431287, + "share_down": 0.5894291754756871, + "n": 2365 + } + }, + "trailing-year state multiplier c=0.1": { + "nse": { + "name": "trailing-year state multiplier c=0.1", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7359820843113118, + "pop_change": -0.0031298453441799268, + "ci_lo": -0.004941113158876237, + "ci_hi": -0.0004350331759627253, + "paired_median": -0.001278026726900694, + "share_up": 0.40380549682875266, + "share_down": 0.5961945031712473, + "n": 2365 + }, + "kge": { + "name": "trailing-year state multiplier c=0.1", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7632815193848514, + "pop_change": 0.004208036815239735, + "ci_lo": 0.0009192809717952444, + "ci_hi": 0.006516819588001204, + "paired_median": 0.0033724987095145043, + "share_up": 0.6109936575052854, + "share_down": 0.3890063424947146, + "n": 2365 + } + }, + " sensitivity: state multiplier c=0.2 (not fitted)": { + "nse": { + "name": " sensitivity: state multiplier c=0.2 (not fitted)", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7306314035591972, + "pop_change": -0.008480526096294572, + "ci_lo": -0.011908249991319009, + "ci_hi": -0.004783248465052079, + "paired_median": -0.005447714496204514, + "share_up": 0.32346723044397463, + "share_down": 0.6765327695560254, + "n": 2365 + }, + "kge": { + "name": " sensitivity: state multiplier c=0.2 (not fitted)", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7619661826755945, + "pop_change": 0.0028927001059827484, + "ci_lo": -0.002218156021173592, + "ci_hi": 0.006500920268556788, + "paired_median": 0.0042874520074797084, + "share_up": 0.572938689217759, + "share_down": 0.427061310782241, + "n": 2365 + } + }, + " sensitivity: state multiplier c=0.4 (not fitted)": { + "nse": { + "name": " sensitivity: state multiplier c=0.4 (not fitted)", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7099762453613981, + "pop_change": -0.029135684294093633, + "ci_lo": -0.03423242409409916, + "ci_hi": -0.024050104831563137, + "paired_median": -0.022663429171815186, + "share_up": 0.20084566596194503, + "share_down": 0.7991543340380549, + "n": 2365 + }, + "kge": { + "name": " sensitivity: state multiplier c=0.4 (not fitted)", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.7419364747405517, + "pop_change": -0.017137007829060025, + "ci_lo": -0.02249918499920909, + "ci_hi": -0.011214290904320155, + "paired_median": -0.0026341571026100574, + "share_up": 0.4824524312896406, + "share_down": 0.5175475687103595, + "n": 2365 + } + }, + "per-gauge quantile mapping (gauged ceiling)": { + "nse": { + "name": "per-gauge quantile mapping (gauged ceiling)", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.7353406672520629, + "pop_change": -0.0037712624034288433, + "ci_lo": -0.008461165065746318, + "ci_hi": 0.0015463202944585401, + "paired_median": -0.0005684344638307026, + "share_up": 0.4887949260042283, + "share_down": 0.5112050739957716, + "n": 2365 + }, + "kge": { + "name": "per-gauge quantile mapping (gauged ceiling)", + "metric": "KGE", + "median_base": 0.7591488667927256, + "median_new": 0.8016172290251325, + "pop_change": 0.04246836223240691, + "ci_lo": 0.0354016569917804, + "ci_hi": 0.04840913740074113, + "paired_median": 0.035620809559510136, + "share_up": 0.6954314720812182, + "share_down": 0.30456852791878175, + "n": 2364 + } + }, + "global quantile mapping on p / pbar": { + "nse": { + "name": "global quantile mapping on p / pbar", + "metric": "NSE", + "median_base": 0.7391119296554918, + "median_new": 0.6917523186990105, + "pop_change": -0.047359610956481224, + "ci_lo": -0.053759979384448375, + "ci_hi": -0.04195892620181558, + "paired_median": -0.0398964670721772, + "share_up": 0.22832980972515857, + "share_down": 0.7716701902748414, + "n": 2365 + }, + "kge": { + "name": "global quantile mapping on p / pbar", + "metric": "KGE", + "median_base": 0.7590734825696117, + "median_new": 0.735208579123646, + "pop_change": -0.0238649034459657, + "ci_lo": -0.030767823038077652, + "ci_hi": -0.01544526840506627, + "paired_median": -0.004901993875675248, + "share_up": 0.48668076109936576, + "share_down": 0.5133192389006342, + "n": 2365 + } + } +} \ No newline at end of file diff --git a/experiments/levers/volume_state_s42.txt b/experiments/levers/volume_state_s42.txt new file mode 100644 index 00000000..cb86df21 --- /dev/null +++ b/experiments/levers/volume_state_s42.txt @@ -0,0 +1,16 @@ +== seed 42: state-dependent corrections, fit WY1982-1995, score WY1996-2010 (base 0.7391 / 0.7591) + power law fit: a 0.96, b 1.0 (training median 0.7855 vs 0.7847) + power law a=0.96 b=1.0 NSE median 0.7391 -> 0.7418 pop +0.0026 [+0.0008,+0.0066] paired +0.0023 up 57.2% down 42.8% + power law a=0.96 b=1.0 KGE median 0.7591 -> 0.7584 pop -0.0006 [-0.0060,+0.0032] paired -0.0106 up 41.1% down 58.9% + trailing-year state exponent c = 0.1 (training median 0.7847) + trailing-year state multiplier c=0.1 NSE median 0.7391 -> 0.7360 pop -0.0031 [-0.0049,-0.0004] paired -0.0013 up 40.4% down 59.6% + trailing-year state multiplier c=0.1 KGE median 0.7591 -> 0.7633 pop +0.0042 [+0.0009,+0.0065] paired +0.0034 up 61.1% down 38.9% + sensitivity: state multiplier c=0.2 (not fitted) NSE median 0.7391 -> 0.7306 pop -0.0085 [-0.0119,-0.0048] paired -0.0054 up 32.3% down 67.7% + sensitivity: state multiplier c=0.2 (not fitted) KGE median 0.7591 -> 0.7620 pop +0.0029 [-0.0022,+0.0065] paired +0.0043 up 57.3% down 42.7% + sensitivity: state multiplier c=0.4 (not fitted) NSE median 0.7391 -> 0.7100 pop -0.0291 [-0.0342,-0.0241] paired -0.0227 up 20.1% down 79.9% + sensitivity: state multiplier c=0.4 (not fitted) KGE median 0.7591 -> 0.7419 pop -0.0171 [-0.0225,-0.0112] paired -0.0026 up 48.2% down 51.8% + per-gauge quantile mapping (gauged ceiling) NSE median 0.7391 -> 0.7353 pop -0.0038 [-0.0085,+0.0015] paired -0.0006 up 48.9% down 51.1% + per-gauge quantile mapping (gauged ceiling) KGE median 0.7591 -> 0.8016 pop +0.0425 [+0.0354,+0.0484] paired +0.0356 up 69.5% down 30.5% + global normalized quantiles (pred -> obs): q05 0.073->0.025, q50 0.574->0.513, q95 3.181->3.434 + global quantile mapping on p / pbar NSE median 0.7391 -> 0.6918 pop -0.0474 [-0.0538,-0.0420] paired -0.0399 up 22.8% down 77.2% + global quantile mapping on p / pbar KGE median 0.7591 -> 0.7352 pop -0.0239 [-0.0308,-0.0154] paired -0.0049 up 48.7% down 51.3% diff --git a/experiments/levers/volume_state_s43.json b/experiments/levers/volume_state_s43.json new file mode 100644 index 00000000..37ae27b9 --- /dev/null +++ b/experiments/levers/volume_state_s43.json @@ -0,0 +1,170 @@ +{ + "power law a=1.0 b=1.0": { + "nse": { + "name": "power law a=1.0 b=1.0", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7341072383216394, + "pop_change": -1.1102230246251565e-16, + "ci_lo": -6.661338147750939e-16, + "ci_hi": 2.220446049250313e-16, + "paired_median": -1.1102230246251565e-16, + "share_up": 0.0, + "share_down": 0.0, + "n": 2365 + }, + "kge": { + "name": "power law a=1.0 b=1.0", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7587949000037071, + "pop_change": 0.0, + "ci_lo": -1.1102230246251565e-16, + "ci_hi": 0.0, + "paired_median": 0.0, + "share_up": 0.0, + "share_down": 0.0, + "n": 2365 + } + }, + "trailing-year state multiplier c=0.0": { + "nse": { + "name": "trailing-year state multiplier c=0.0", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7341072383216395, + "pop_change": 0.0, + "ci_lo": 0.0, + "ci_hi": 0.0, + "paired_median": 0.0, + "share_up": 0.0, + "share_down": 0.0, + "n": 2365 + }, + "kge": { + "name": "trailing-year state multiplier c=0.0", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7587949000037071, + "pop_change": 0.0, + "ci_lo": 0.0, + "ci_hi": 0.0, + "paired_median": 0.0, + "share_up": 0.0, + "share_down": 0.0, + "n": 2365 + } + }, + " sensitivity: state multiplier c=0.2 (not fitted)": { + "nse": { + "name": " sensitivity: state multiplier c=0.2 (not fitted)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7259650635484285, + "pop_change": -0.008142174773210997, + "ci_lo": -0.01136704583582585, + "ci_hi": -0.005756398636913876, + "paired_median": -0.005245187789434502, + "share_up": 0.3230443974630021, + "share_down": 0.6769556025369979, + "n": 2365 + }, + "kge": { + "name": " sensitivity: state multiplier c=0.2 (not fitted)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7611749724155825, + "pop_change": 0.0023800724118754335, + "ci_lo": -0.00328288909525242, + "ci_hi": 0.005125891618384833, + "paired_median": 0.004693831986358199, + "share_up": 0.5813953488372093, + "share_down": 0.4186046511627907, + "n": 2365 + } + }, + " sensitivity: state multiplier c=0.4 (not fitted)": { + "nse": { + "name": " sensitivity: state multiplier c=0.4 (not fitted)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.705687022932169, + "pop_change": -0.028420215389470482, + "ci_lo": -0.033090738672867014, + "ci_hi": -0.02421083388825819, + "paired_median": -0.022177596388774368, + "share_up": 0.19830866807610995, + "share_down": 0.8016913319238901, + "n": 2365 + }, + "kge": { + "name": " sensitivity: state multiplier c=0.4 (not fitted)", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7431002052696534, + "pop_change": -0.015694694734053716, + "ci_lo": -0.02248334906575715, + "ci_hi": -0.01088004329819386, + "paired_median": -0.0007717345108442242, + "share_up": 0.492600422832981, + "share_down": 0.507399577167019, + "n": 2365 + } + }, + "per-gauge quantile mapping (gauged ceiling)": { + "nse": { + "name": "per-gauge quantile mapping (gauged ceiling)", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.7307504196435757, + "pop_change": -0.003356818678063811, + "ci_lo": -0.007706443524997561, + "ci_hi": 0.002211089520133029, + "paired_median": -0.00107734010881666, + "share_up": 0.4799154334038055, + "share_down": 0.5200845665961945, + "n": 2365 + }, + "kge": { + "name": "per-gauge quantile mapping (gauged ceiling)", + "metric": "KGE", + "median_base": 0.759074685078559, + "median_new": 0.7995393818234833, + "pop_change": 0.040464696744924256, + "ci_lo": 0.03301960968300646, + "ci_hi": 0.046429539674802955, + "paired_median": 0.03469210289034258, + "share_up": 0.6983925549915397, + "share_down": 0.30160744500846026, + "n": 2364 + } + }, + "global quantile mapping on p / pbar": { + "nse": { + "name": "global quantile mapping on p / pbar", + "metric": "NSE", + "median_base": 0.7341072383216395, + "median_new": 0.683964945720151, + "pop_change": -0.0501422926014885, + "ci_lo": -0.05783074754014096, + "ci_hi": -0.04333746450287676, + "paired_median": -0.04246661884187952, + "share_up": 0.23002114164904863, + "share_down": 0.7699788583509514, + "n": 2365 + }, + "kge": { + "name": "global quantile mapping on p / pbar", + "metric": "KGE", + "median_base": 0.7587949000037071, + "median_new": 0.7323998381712952, + "pop_change": -0.026395061832411915, + "ci_lo": -0.03453191975663622, + "ci_hi": -0.01888327462967276, + "paired_median": -0.003860152247207038, + "share_up": 0.4896405919661734, + "share_down": 0.5103594080338266, + "n": 2365 + } + } +} \ No newline at end of file diff --git a/experiments/levers/volume_state_s43.txt b/experiments/levers/volume_state_s43.txt new file mode 100644 index 00000000..171d1cd8 --- /dev/null +++ b/experiments/levers/volume_state_s43.txt @@ -0,0 +1,16 @@ +== seed 43: state-dependent corrections, fit WY1982-1995, score WY1996-2010 (base 0.7341 / 0.7588) + power law fit: a 1.0, b 1.0 (training median 0.7822 vs 0.7822) + power law a=1.0 b=1.0 NSE median 0.7341 -> 0.7341 pop -0.0000 [-0.0000,+0.0000] paired -0.0000 up 0.0% down 0.0% + power law a=1.0 b=1.0 KGE median 0.7588 -> 0.7588 pop +0.0000 [-0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + trailing-year state exponent c = 0.0 (training median 0.7822) + trailing-year state multiplier c=0.0 NSE median 0.7341 -> 0.7341 pop +0.0000 [+0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + trailing-year state multiplier c=0.0 KGE median 0.7588 -> 0.7588 pop +0.0000 [+0.0000,+0.0000] paired +0.0000 up 0.0% down 0.0% + sensitivity: state multiplier c=0.2 (not fitted) NSE median 0.7341 -> 0.7260 pop -0.0081 [-0.0114,-0.0058] paired -0.0052 up 32.3% down 67.7% + sensitivity: state multiplier c=0.2 (not fitted) KGE median 0.7588 -> 0.7612 pop +0.0024 [-0.0033,+0.0051] paired +0.0047 up 58.1% down 41.9% + sensitivity: state multiplier c=0.4 (not fitted) NSE median 0.7341 -> 0.7057 pop -0.0284 [-0.0331,-0.0242] paired -0.0222 up 19.8% down 80.2% + sensitivity: state multiplier c=0.4 (not fitted) KGE median 0.7588 -> 0.7431 pop -0.0157 [-0.0225,-0.0109] paired -0.0008 up 49.3% down 50.7% + per-gauge quantile mapping (gauged ceiling) NSE median 0.7341 -> 0.7308 pop -0.0034 [-0.0077,+0.0022] paired -0.0011 up 48.0% down 52.0% + per-gauge quantile mapping (gauged ceiling) KGE median 0.7591 -> 0.7995 pop +0.0405 [+0.0330,+0.0464] paired +0.0347 up 69.8% down 30.2% + global normalized quantiles (pred -> obs): q05 0.074->0.025, q50 0.575->0.514, q95 3.177->3.447 + global quantile mapping on p / pbar NSE median 0.7341 -> 0.6840 pop -0.0501 [-0.0578,-0.0433] paired -0.0425 up 23.0% down 77.0% + global quantile mapping on p / pbar KGE median 0.7588 -> 0.7324 pop -0.0264 [-0.0345,-0.0189] paired -0.0039 up 49.0% down 51.0% From b95661487cf3f7c5ba015e118e3b7a629408c49d Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Wed, 30 Sep 2026 14:11:04 -0400 Subject: [PATCH 4/5] full population: S7base dam recipe vs no-dam twins, two seeds; journal Dammed gauges +0.0112 / +0.0066 paired NSE (seeds 42 / 43); population median +0.0093 / -0.0016, about +0.004 on average, inside seed noise. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015TepsZAS2LkFC5EmvuZrz9 --- .../full_run/paired_full_run_full_s7_s42.csv | 2366 +++++++++++++++++ .../full_run/paired_full_run_full_s7_s42.json | 193 ++ .../full_run/paired_full_run_full_s7_s43.csv | 2366 +++++++++++++++++ .../full_run/paired_full_run_full_s7_s43.json | 193 ++ research/journal/2026-09.md | 20 + 5 files changed, 5138 insertions(+) create mode 100644 experiments/reservoir/full_run/paired_full_run_full_s7_s42.csv create mode 100644 experiments/reservoir/full_run/paired_full_run_full_s7_s42.json create mode 100644 experiments/reservoir/full_run/paired_full_run_full_s7_s43.csv create mode 100644 experiments/reservoir/full_run/paired_full_run_full_s7_s43.json diff --git a/experiments/reservoir/full_run/paired_full_run_full_s7_s42.csv b/experiments/reservoir/full_run/paired_full_run_full_s7_s42.csv new file mode 100644 index 00000000..2647ee26 --- /dev/null +++ b/experiments/reservoir/full_run/paired_full_run_full_s7_s42.csv @@ -0,0 +1,2366 @@ +STAID,nse_off,kge_off,nse_learned,kge_learned,nse_base,kge_base,area_km2,n_nid,n_nid_ge10mcm,nid_dor,nid_on_gauge_reach,nwm_cls,smoke_role,dnse,dkge +14190500,0.8935149412764634,0.921663620028006,0.8927476079406207,0.9206187299184921,0.8754681248901153,0.8481105177223798,603.4942,0,0,0.0,False,none,control,-0.0007673333358426682,-0.0010448901095138075 +14301000,0.9495858508794389,0.9428465765878717,0.9500830545139257,0.9413264482609721,0.9427664461323382,0.9426347664403104,1743.544,1,0,0.000514202955206,False,DOR<=0.1,,0.000497203634486798,-0.001520128326899628 +14301500,0.8912302449568082,0.7647714573041755,0.8911747528493494,0.7640217932002098,0.8966158369797994,0.7907410989413804,417.5676,0,0,0.0,False,none,control,-5.549210745880995e-05,-0.0007496641039657082 +14305500,0.8604987704687564,0.7960722303318298,0.8622052321433414,0.7998920925592463,0.8771296428001245,0.9170402747219505,526.3299,0,0,0.0,False,none,control,0.0017064616745849914,0.0038198622274164817 +14150000,0.433424112307236,0.582524810705669,0.5532046169017198,0.6291717448223941,0.38703881704641907,0.5794476882624595,2605.191,3,3,0.3781061174872411,True,0.10.5,,-0.0016930708445430653,-0.0017692877158879128 +14309000,-0.06623762214341422,0.5006763489148962,-0.016674955749813813,0.5168532925661675,-0.9975025757364964,0.09744610866197556,202.1301,1,1,0.5990075366723981,True,DOR>0.5,,0.04956266639360041,0.016176943651271314 +14309500,0.7557517468790099,0.8065107517073318,0.7503222924553654,0.8048384141655383,0.7712974850721274,0.7923413447206061,224.9208,0,0,0.0,False,none,control,-0.005429454423644509,-0.0016723375417935227 +14310000,0.8775296315194198,0.88728963370603,0.8751535575622656,0.8811237073464377,0.7972043931678076,0.7076519727415477,1194.809,1,1,0.0777145152918871,False,DOR<=0.1,,-0.0023760739571541123,-0.006165926359592233 +14312000,0.9309920104586267,0.9625828850251464,0.9355708485154846,0.9671973415034206,0.8983174686269579,0.9040986114354737,4234.129,2,2,0.0281940264943907,False,DOR<=0.1,,0.004578838056857881,0.004614456478274187 +14316700,0.8937905357333471,0.9234497745019072,0.8860238169462624,0.9189969471139768,0.8826049874840111,0.9329542156396854,587.8989,0,0,0.0,False,none,control,-0.007766718787084681,-0.0044528273879304026 +14325000,0.8114033802827247,0.83215511845368,0.8124350320199464,0.8394317221318863,0.729998135490185,0.6418288575606694,443.0745,0,0,0.0,False,none,control,0.0010316517372216394,0.0072766036782063725 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+11527000,0.6988306071537642,0.8168267334716572,0.702386013040895,0.7980512790208324,0.6302777460860245,0.8092505339749837,3727.024,3,2,1.5666560634485385,False,DOR>0.5,dam,0.003555405887130769,-0.018775454450824847 +11374000,0.782604857399225,0.807478487974228,0.7672099591343913,0.7995543086338515,0.7878074756900677,0.8024550944353329,1096.202,0,0,0.0,False,none,control,-0.015394898264833756,-0.007924179340376591 +11379500,0.5301237011535926,0.6703202305649516,0.5143183671236922,0.644079398774722,0.4619881154613019,0.58742731527283,239.7195,0,0,0.0,False,none,control,-0.015805334029900475,-0.026240831790229624 +11383500,0.8769061487461141,0.8020378632151721,0.8748526343843778,0.8028881957042451,0.8894012676959584,0.8076275379609259,539.7753,0,0,0.0,False,none,control,-0.002053514361736286,0.000850332489072958 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100644 --- a/research/journal/2026-09.md +++ b/research/journal/2026-09.md @@ -2078,3 +2078,23 @@ Do not run further leakance arms scored on skill. **Result:** Replay: +0.0146 [+0.0091, +0.0265] (57 up / 12 down) vs offline +0.0139, 0 water created: pass. Training pair: -0.0002 [-0.0005, -0.0000] (69 up / 97 down) at flood-control dams; pools did not grow (median largest fill 0.07 d; 4.7 % of pooled dams above 1 d); 10 dams create >= 5 % water in both S7 arms: fail. Against the no-dam arm, S7base gains +0.0238 at the 117 target gauges and +0.0115 at all 458 dam gauges (S6base +0.0155 / +0.0084; S4v3 +0.0168 / +0.0067); dropping per_dam_l2 alone adds +0.0050 / +0.0037 paired. S7base carried to the 2,365-gauge population by DOR bin: median 0.7391 -> 0.7425 (+0.0034). **Conclusion:** The flood-pool law works in the engine with fitted parameters, but training cannot grow a pool from the off init, so the trained pair is uninformative about the law and the pass bar is not met; full-population runs were not launched. The per-dam L2 at 1e-3 had been suppressing every per-dam parameter; without it the base recipe gains about 50 % more, at the cost of more created water (10 dams >= 5 %). Next options: initialise pools from the offline fits; population runs of the S7base recipe with a water-conserving floor; the population target still needs non-dam levers. + +--- + +### 2026-09-30 · full population: S7base dam recipe vs no-dam twins, joint training, rho 180, two seeds (`2026-09-30T13-16-04Z` / `13-16-11Z` seed 42; `13-27-14Z` / `13-27-22Z` seed 43) + +**Facts** + +- `train-and-test`, CPU, 2,365 gauges, main workspace, binary `ddrs-full-7ccbd19`, configs `config/experiments/dam_release_full_s7_s{42,43}.yaml` and `dam_release_full_nodam_r180_s{42,43}.yaml` (16080ac); each pair differs only by the dam model; joint training (routing head and dam parameters), rho 180, warmup 30, testing warmup 5, 50 epochs +- dam recipe: additive row with the positivity cap, per-dam T0, rule curve (max 0.5), per_dam_l2 0, feasibility penalty 5.0, `dam_floor: carry`, completion years; 1,024 NID dams (947 active in 1981) +- paired analysis: `experiments/reservoir/full_run/paired_full_run_full_s7_s{42,43}.{json,csv}` + +**Question:** On the full population, how much does the best dam recipe move the median NSE toward the +0.03 target, and does the gain below dams hold across seeds? + +**What we did:** + +- Two seeds of dams vs no dams with identical training setup; paired per gauge over the test years; groups by NID dam >= 10 MCM upstream. + +**Result:** Population median NSE: seed 42 0.7327 -> 0.7420 (+0.0093), seed 43 0.7376 -> 0.7360 (-0.0016). Paired median: all gauges +0.0015 / -0.0006; 917 dammed gauges +0.0112 [+0.0089, +0.0135] / +0.0066 [+0.0046, +0.0090]; 1,448 undammed +0.0002 / -0.0021. Paired KGE +0.0002 / -0.0006. Carry floor: owed at the end 3.9e7 / 4.9e7 m3 against 1.25e10 / 1.45e10 m3 repaid. + +**Conclusion:** Supported below dams: about five times the first learned release (+0.0014 / +0.0039 on the same gauges) and consistent across seeds. At the population median the effect averages about +0.004 across seeds, inside the 0.005 seed spread from joint-training drift at undammed gauges; the +0.03 target is not reached by dams, as the ceiling analysis predicted. The population lever is the multi-product Q' blend (`experiments/levers/`), pending the user's decision. From 3708ce40529b15ce6f87b1d02462029c98dabaa9 Mon Sep 17 00:00:00 2001 From: Tadd Bindas Date: Wed, 30 Sep 2026 14:11:44 -0400 Subject: [PATCH 5/5] journal: the six levers replays (training-year and checkpoint replays) Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_015TepsZAS2LkFC5EmvuZrz9 --- research/journal/2026-09.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/research/journal/2026-09.md b/research/journal/2026-09.md index f08f05c6..0c28b3a5 100644 --- a/research/journal/2026-09.md +++ b/research/journal/2026-09.md @@ -2098,3 +2098,24 @@ Do not run further leakance arms scored on skill. **Result:** Population median NSE: seed 42 0.7327 -> 0.7420 (+0.0093), seed 43 0.7376 -> 0.7360 (-0.0016). Paired median: all gauges +0.0015 / -0.0006; 917 dammed gauges +0.0112 [+0.0089, +0.0135] / +0.0066 [+0.0046, +0.0090]; 1,448 undammed +0.0002 / -0.0021. Paired KGE +0.0002 / -0.0006. Carry floor: owed at the end 3.9e7 / 4.9e7 m3 against 1.25e10 / 1.45e10 m3 repaid. **Conclusion:** Supported below dams: about five times the first learned release (+0.0014 / +0.0039 on the same gauges) and consistent across seeds. At the population median the effect averages about +0.004 across seeds, inside the 0.005 seed spread from joint-training drift at undammed gauges; the +0.03 target is not reached by dams, as the ceiling analysis predicted. The population lever is the multi-product Q' blend (`experiments/levers/`), pending the user's decision. + +--- + +### 2026-09-30 · non-dam levers: six test-only replays for honest fitting (`2026-09-30T13-13-59Z`, `13-50-59Z`, `14-30-08Z`, `14-56-59Z`, `15-22-40Z`, `15-35-31Z`) + +**Facts** + +- zero-step resumes (no training), CPU, main workspace, binary `ddrs-v6-77aa4a0`, launched by the levers subagent with `DDRS_JOURNAL_OFF=1`; analysis and pre-registration in `experiments/levers/` (report.txt, prereg.txt) +- `13-13-59Z` / `13-50-59Z`: the seed-42 / seed-43 no-dam checkpoints (`07-29-47Z`, `10-31-30Z`, epoch_50_mb_9) scored over the training years 1981-10-01..1996-09-30 +- `14-30-08Z` / `14-56-59Z`: the daily-LSTM and distributed-dHBV2 (DIST) routed runs scored over the training years, to fit ensemble weights +- `15-22-40Z` / `15-35-31Z`: the seed-42 no-dam epoch-25 and epoch-40 checkpoints scored on WY1996-2000, for convergence + +**Question:** Which non-dam levers can move the 2,365-gauge population median NSE toward +0.03 when fitted on training years and scored on test years? + +**What we did:** + +- Produced training-year predictions (none existed) to fit runoff multipliers and product-ensemble weights honestly; replayed intermediate checkpoints to test convergence. + +**Result:** Model volume is unbiased on training years (pred/obs 1.004 / 1.001); honest global, per-HUC2 and attribute-based multipliers move the population median by -0.0039 to +0.0008 (+0.0005 pre-registered). Epoch 25 -> 40 -> 50 on WY1996-2000: 0.7445 -> 0.7462 -> 0.7474. A three-product blend (UH 0.50, DIST 0.25, LSTM 0.25, weights from the training-year replays) gives +0.0259 [+0.0221, +0.0309] at the population median, KGE about flat. + +**Conclusion:** Supported: the runoff multiplier is not a lever (the test-period volume excess is a drought/drift the training years cannot see); training is near a plateau (+0.001 per 10 epochs); the multi-product runoff blend is the only lever that reaches the +0.03 population target, with gamma = 0 and the dam recipe adding a few thousandths each. Decision on building it pending with the user.